1091 lines
48 KiB
JSON
1091 lines
48 KiB
JSON
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"custom_extraction_instructions": "This is a educational document in prose format. Summary: A document discussing the growth and impact of a Digital Design & Fabrication program, highlighting its focus on computational design, additive manufacturing, employer partnerships, and 100% employment rate for graduates. This metadata is provided to orient your extraction, not to constrain it. Extract entities and relationships freely from the document text itself; the metadata is descriptive context, not a checklist.",
|
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"submit_elapsed_s": 21.9,
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"submit_result": {
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"ok": true
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{
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"name": "Aaron AI: So, I've been working on the RNAI project, and the way I've ...",
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|
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},
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"metadata_elapsed_s": 48.2,
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"custom_extraction_instructions": "This is a technical document in prose format. Summary: Document discusses the use of Claude Code for a RNAI project and potential security concerns when using it. This metadata is provided to orient your extraction, not to constrain it. Extract entities and relationships freely from the document text itself; the metadata is descriptive context, not a checklist.",
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"submit_elapsed_s": 22.8,
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"submit_result": {
|
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"ok": true
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},
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{
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"name": "Claude: Internship agreement writing help",
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"bucket": "mid",
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"has_dates": false
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},
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"has_named_people": true,
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"has_technical_terminology": true,
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"has_first_person": false,
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"has_quotations": false
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},
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"domain_class": "educational",
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"one_sentence_summary": "Conversation about writing internship agreements for SUNY New Paltz's Digital Design & Fabrication program"
|
|
},
|
|
"metadata_elapsed_s": 45.7,
|
|
"custom_extraction_instructions": "This is a educational document in structured format. Summary: Conversation about writing internship agreements for SUNY New Paltz's Digital Design & Fabrication program This metadata is provided to orient your extraction, not to constrain it. Extract entities and relationships freely from the document text itself; the metadata is descriptive context, not a checklist.",
|
|
"submit_elapsed_s": 13.4,
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"submit_result": {
|
|
"ok": true
|
|
}
|
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},
|
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{
|
|
"name": "ChatGPT: Research Statement Restructure",
|
|
"bucket": "mid",
|
|
"tier1_entities": 8,
|
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"doc_chars": 9250,
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"metadata": {
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"language": "en",
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"char_length": 9250,
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"primary_format": "mixed",
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"structural_signals": {
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"has_headings": true,
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"has_bullet_lists": false,
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"has_numbered_lists": false,
|
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"has_tables": false,
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"has_code_blocks": false,
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"has_dates": false
|
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},
|
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"content_signals": {
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"has_named_people": true,
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"has_technical_terminology": true,
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"has_first_person": true,
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"has_quotations": false
|
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},
|
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"domain_class": "educational",
|
|
"one_sentence_summary": "A conversation between Aaron and ChatGPT about restructuring a research statement and revising a philosophy of teaching for a job application"
|
|
},
|
|
"metadata_elapsed_s": 84.0,
|
|
"custom_extraction_instructions": "This is a educational document in mixed format. Summary: A conversation between Aaron and ChatGPT about restructuring a research statement and revising a philosophy of teaching for a job application This metadata is provided to orient your extraction, not to constrain it. Extract entities and relationships freely from the document text itself; the metadata is descriptive context, not a checklist.",
|
|
"submit_elapsed_s": 19.5,
|
|
"submit_result": {
|
|
"ok": true
|
|
}
|
|
},
|
|
{
|
|
"name": "ChatGPT: Sink Sprayer Fitting Name",
|
|
"bucket": "low",
|
|
"tier1_entities": 5,
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"doc_chars": 1383,
|
|
"metadata": {
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"language": "en",
|
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"char_length": 1383,
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"primary_format": "conversational",
|
|
"structural_signals": {
|
|
"has_headings": false,
|
|
"has_bullet_lists": false,
|
|
"has_numbered_lists": false,
|
|
"has_tables": false,
|
|
"has_code_blocks": false,
|
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"has_dates": false
|
|
},
|
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"content_signals": {
|
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"has_named_people": true,
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"has_institutional_language": false,
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|
"has_technical_terminology": true,
|
|
"has_first_person": true,
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|
"has_quotations": false
|
|
},
|
|
"domain_class": "personal",
|
|
"one_sentence_summary": "Discussion about the name and fitting components of a sink sprayer"
|
|
},
|
|
"metadata_elapsed_s": 22.5,
|
|
"custom_extraction_instructions": "This is a personal document in conversational format. Summary: Discussion about the name and fitting components of a sink sprayer This metadata is provided to orient your extraction, not to constrain it. Extract entities and relationships freely from the document text itself; the metadata is descriptive context, not a checklist.",
|
|
"submit_elapsed_s": 9.5,
|
|
"submit_result": {
|
|
"ok": true
|
|
}
|
|
},
|
|
{
|
|
"name": "ChatGPT: Title: User request summary.",
|
|
"bucket": "low",
|
|
"tier1_entities": 5,
|
|
"doc_chars": 4363,
|
|
"metadata": {
|
|
"language": "en",
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"char_length": 4363,
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"primary_format": "mixed",
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"structural_signals": {
|
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"has_headings": true,
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|
"has_bullet_lists": false,
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|
"has_numbered_lists": false,
|
|
"has_tables": false,
|
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"has_code_blocks": false,
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|
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},
|
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"content_signals": {
|
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"has_named_people": true,
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|
"has_technical_terminology": false,
|
|
"has_first_person": true,
|
|
"has_quotations": false
|
|
},
|
|
"domain_class": "personal",
|
|
"one_sentence_summary": "Conversation between a user and an assistant discussing the creation of an introductory speech for a Design Week event"
|
|
},
|
|
"metadata_elapsed_s": 47.9,
|
|
"custom_extraction_instructions": "This is a personal document in mixed format. Summary: Conversation between a user and an assistant discussing the creation of an introductory speech for a Design Week event This metadata is provided to orient your extraction, not to constrain it. Extract entities and relationships freely from the document text itself; the metadata is descriptive context, not a checklist.",
|
|
"submit_elapsed_s": 14.0,
|
|
"submit_result": {
|
|
"ok": true
|
|
}
|
|
},
|
|
{
|
|
"name": "ChatGPT: Regex for inserting letters",
|
|
"bucket": "low",
|
|
"tier1_entities": 5,
|
|
"doc_chars": 12778,
|
|
"metadata": {
|
|
"operation": "replace",
|
|
"searchValue": "Z 0",
|
|
"replaceValue": "Z 25",
|
|
"char_length": 12000
|
|
},
|
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"metadata_elapsed_s": 141.9,
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"custom_extraction_instructions": "This is a unknown document in unknown format. Summary: This metadata is provided to orient your extraction, not to constrain it. Extract entities and relationships freely from the document text itself; the metadata is descriptive context, not a checklist.",
|
|
"submit_elapsed_s": 10.8,
|
|
"submit_result": {
|
|
"ok": true
|
|
}
|
|
},
|
|
{
|
|
"name": "ChatGPT: Provide DXF file",
|
|
"bucket": "low",
|
|
"tier1_entities": 5,
|
|
"doc_chars": 38594,
|
|
"metadata": {
|
|
"role": "assistant",
|
|
"usage": {
|
|
"assistant": 1
|
|
},
|
|
"messages": [
|
|
{
|
|
"role": "assistant",
|
|
"content": "Here's the code to convert the existing stroke-only SVG polylines into curved SVG paths using cubic Beziers (Catmull\u2013Rom smoothing):"
|
|
},
|
|
{
|
|
"role": "assistant",
|
|
"content": "First, we'll import the required libraries and parse the SVG file."
|
|
},
|
|
{
|
|
"role": "assistant",
|
|
"content": "```javascript\nconst svgjs = require('svg.js');\nlet doc = svgjs('path-to-your-svg-file');\n```"
|
|
},
|
|
{
|
|
"role": "assistant",
|
|
"content": "Next, we'll loop through all the paths in the SVG and replace them with smoothed cubic Bezier curves."
|
|
},
|
|
{
|
|
"role": "assistant",
|
|
"content": "```javascript\nfor (let path of doc.find('path')) {\n // Calculate control points for Catmull\u2013Rom splines\n const tangentsBefore = getTangents(path);\nconst controlPoints = calculateControlPoints(tangentsBefore, 4);\n\n // Replace the original path with a new one using cubic Bezier curves\n const smoothedPath = doc.path().moveTo(controlPoints[0].x, controlPoints[0].y).cubic(4, \n controlPoints[1].x - controlPoints[0].x, controlPoints[1].y - controlPoints[0].y, \n controlPoints[2].x - controlPoints[1].x, controlPoints[2].y - controlPoints[1].y, \n controlPoints[3].x - controlPoints[2].x, controlPoints[3].y - controlPoints[2].y);\n\n // Replace the original path with the new smoothed one\n path.remove().insertBefore(smoothedPath);\n}\n\nfunction getTangents(path) {\n const points = path.bbox().getPoints();\nconst tangents = [];\nfor (let i = 1; i < points.length - 1; i++) {\n const dx = points[i].x - points[i-1].x;\n const dy = points[i].y - points[i-1].y;\n tangents.push({dx, dy});\n}\nreturn tangents;\n}\n\nfunction calculateControlPoints(tangents, segmentCount) {\n const controlPoints = [];\nfor (let i = 0; i < tangents.length - 2 * segmentCount + 1; i += 4) {\n // Calculate four Catmull\u2013Rom spline control points for the next segment\n const p0 = tangents[i];\nconst p1 = tangents[i+1];\nconst p2 = tangents[i+2];\nconst p3 = tangents[i+3];\n\ncontrolPoints.push(getBezierControlPoint(p0, p1, p2, p3));\n}\nreturn controlPoints;\n}\n\nfunction getBezierControlPoint(p0, p1, p2, p3) {\n // Hermite curve calculation for each point between p0 and p1,\np2, and p3.\n const t = 0.5;\n\n// First derivative of the Hermite polynomial at p0:\n const d0x = 2 * (p1.x - p0.x) - t * (t * (2 * p0.x - 3 * p1.x + 2 * p2.x) - (2 - t) * (p1.x - p2.x));\nconst d0y = 2 * (p1.y - p0.y) - t * (t * (2 * p0.y - 3 * p1.y + 2 * p2.y) - (2 - t) * (p1.y - p2.y));\n\n// First derivative of the Hermite polynomial at p3:\n const d3x = 2 * (p3.x - p2.x) - t * (t * (2 * p2.x - 3 * p3.x + 2 * p0.x) - (2 - t) * (p2.x - p0.x));\nconst d3y = 2 * (p3.y - p2.y) - t * (t * (2 * p2.y - 3 * p3.y + 2 * p0.y) - (2 - t) * (p2.y - p0.y));\n\n// Calculate the control points:\n const c0 = {x: p0.x + d0x / 6, y: p0.y + d0y / 6};\nconst c1 = {x: (p1.x + p2.x) / 2, y: (p1.y + p2.y) / 2};\nconst c2 = {x: p3.x + d3x / 6, y: p3.y + d3y / 6};\nreturn {x: (c0.x + c1.x + c2.x) / 3, y: (c0.y + c1.y + c2.y) / 3};\n}\n```"
|
|
}
|
|
],
|
|
"char_length": 12000
|
|
},
|
|
"metadata_elapsed_s": 281.4,
|
|
"custom_extraction_instructions": "This is a unknown document in unknown format. Summary: This metadata is provided to orient your extraction, not to constrain it. Extract entities and relationships freely from the document text itself; the metadata is descriptive context, not a checklist.",
|
|
"submit_elapsed_s": 28.7,
|
|
"submit_result": {
|
|
"ok": true
|
|
}
|
|
},
|
|
{
|
|
"name": "ChatGPT: Rectangle Edge Offset Algorithm",
|
|
"bucket": "low",
|
|
"tier1_entities": 3,
|
|
"doc_chars": 12610,
|
|
"metadata": {
|
|
"response_text": "Here is the requested information in JSON format for the analysis:",
|
|
"json_data": {
|
|
"primary_text": "Offsetting Rectangles with a Specific Distance",
|
|
"sub_title": "Calculation of d for a specific perpendicular offset \u03b4",
|
|
"content": [
|
|
{
|
|
"heading": "2D Case (Standard Rectangle)",
|
|
"steps": [
|
|
{
|
|
"description": "Determine the angle \u03b8 between one of the edges and the bisector vector.",
|
|
"value": "\u03b8 = 45\u00b0"
|
|
},
|
|
{
|
|
"description": "Relate d to the perpendicular offset \u03b4",
|
|
"value": "d = \u03b4 * sqrt(2)"
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"heading": "General Case (Arbitrary Rectangles in 2D or 3D)",
|
|
"steps": [
|
|
{
|
|
"description": "Calculate the angle \u03b8 between one of the rectangle\u2019s edges and the bisector vector.",
|
|
"value": "cos(\u03b8) = vec{u} . vec{b} / (|vec{u}| |vec{b}|)"
|
|
},
|
|
{
|
|
"description": "Determine d",
|
|
"value": "d = \u03b4 / sin(\u03b8)"
|
|
}
|
|
]
|
|
}
|
|
],
|
|
"additional_information": [
|
|
{
|
|
"heading": "Bisector Vector Calculation",
|
|
"steps": [
|
|
{
|
|
"description": "Add the unit vectors of the two edges to get a vector that lies between them:",
|
|
"value": "\u03b2 = \u03c3{u} + \u03c3{v}"
|
|
},
|
|
{
|
|
"description": "Normalize the resulting vector to convert it into a unit vector (this is the bisector vector):",
|
|
"value": "\u03c3{\u03b2} = \u03c3(\u03b2) / |\u03b2|"
|
|
},
|
|
{
|
|
"description": "In 3D space, the angle between the edge vector and bisector may not be 45\u00b0. In this case, use the calculated angle \u03b8 to find d as described in the General Case."
|
|
}
|
|
]
|
|
}
|
|
],
|
|
"primary_keywords": [
|
|
"offsetting",
|
|
"rectangles",
|
|
"specific distance"
|
|
],
|
|
"secondary_keywords": [
|
|
"bisector vector",
|
|
"perpendicular offset",
|
|
"angle",
|
|
"vector calculation"
|
|
]
|
|
},
|
|
"char_length": 12000
|
|
},
|
|
"metadata_elapsed_s": 201.7,
|
|
"custom_extraction_instructions": "This is a unknown document in unknown format. Summary: This metadata is provided to orient your extraction, not to constrain it. Extract entities and relationships freely from the document text itself; the metadata is descriptive context, not a checklist.",
|
|
"submit_elapsed_s": 17.5,
|
|
"submit_result": {
|
|
"ok": true
|
|
}
|
|
},
|
|
{
|
|
"name": "ChatGPT: Testing Vector Alignment",
|
|
"bucket": "low",
|
|
"tier1_entities": 3,
|
|
"doc_chars": 2209,
|
|
"metadata": {
|
|
"language": "en",
|
|
"char_length": 2209,
|
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"primary_format": "prose",
|
|
"structural_signals": {
|
|
"has_headings": true,
|
|
"has_bullet_lists": false,
|
|
"has_numbered_lists": false,
|
|
"has_tables": false,
|
|
"has_code_blocks": false,
|
|
"has_dates": false
|
|
},
|
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"content_signals": {
|
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"has_named_people": false,
|
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"has_institutional_language": false,
|
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"has_technical_terminology": true,
|
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"has_first_person": false,
|
|
"has_quotations": false
|
|
},
|
|
"domain_class": "educational",
|
|
"one_sentence_summary": "This document explains how to test if two vectors are pointing in the same general direction using the dot product."
|
|
},
|
|
"metadata_elapsed_s": 43.2,
|
|
"custom_extraction_instructions": "This is a educational document in prose format. Summary: This document explains how to test if two vectors are pointing in the same general direction using the dot product. This metadata is provided to orient your extraction, not to constrain it. Extract entities and relationships freely from the document text itself; the metadata is descriptive context, not a checklist.",
|
|
"submit_elapsed_s": 31.6,
|
|
"submit_result": {
|
|
"ok": true
|
|
}
|
|
},
|
|
{
|
|
"name": "ChatGPT: Scholarship Recommendation Letter Tips",
|
|
"bucket": "low",
|
|
"tier1_entities": 3,
|
|
"doc_chars": 2980,
|
|
"metadata": {
|
|
"language": "en",
|
|
"char_length": 2980,
|
|
"primary_format": "prose",
|
|
"structural_signals": {
|
|
"has_headings": true,
|
|
"has_bullet_lists": false,
|
|
"has_numbered_lists": false,
|
|
"has_tables": false,
|
|
"has_code_blocks": false,
|
|
"has_dates": false
|
|
},
|
|
"content_signals": {
|
|
"has_named_people": true,
|
|
"has_institutional_language": false,
|
|
"has_technical_terminology": false,
|
|
"has_first_person": true,
|
|
"has_quotations": false
|
|
},
|
|
"domain_class": "educational",
|
|
"one_sentence_summary": "This document provides a comprehensive guide on how to write a scholarship recommendation letter, covering key areas such as introductions, academic and professional qualities, personal characteristics, achievements, future potential, and conclusion."
|
|
},
|
|
"metadata_elapsed_s": 36.3,
|
|
"custom_extraction_instructions": "This is a educational document in prose format. Summary: This document provides a comprehensive guide on how to write a scholarship recommendation letter, covering key areas such as introductions, academic and professional qualities, personal characteristics, achievements, future potential, and conclusion. This metadata is provided to orient your extraction, not to constrain it. Extract entities and relationships freely from the document text itself; the metadata is descriptive context, not a checklist.",
|
|
"submit_elapsed_s": 4.1,
|
|
"submit_result": {
|
|
"ok": true
|
|
}
|
|
},
|
|
{
|
|
"name": "ChatGPT: Respect Individual Interests for Christmas",
|
|
"bucket": "low",
|
|
"tier1_entities": 2,
|
|
"doc_chars": 6126,
|
|
"metadata": {
|
|
"language": "en",
|
|
"char_length": 6126,
|
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|
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"structural_signals": {
|
|
"has_headings": false,
|
|
"has_bullet_lists": true,
|
|
"has_numbered_lists": false,
|
|
"has_tables": false,
|
|
"has_code_blocks": false,
|
|
"has_dates": false
|
|
},
|
|
"content_signals": {
|
|
"has_named_people": true,
|
|
"has_institutional_language": false,
|
|
"has_technical_terminology": false,
|
|
"has_first_person": true,
|
|
"has_quotations": false
|
|
},
|
|
"domain_class": "educational",
|
|
"one_sentence_summary": "Discussion on choosing appropriate Christmas gifts for individuals with unique interests"
|
|
},
|
|
"metadata_elapsed_s": 59.9,
|
|
"custom_extraction_instructions": "This is a educational document in prose format. Summary: Discussion on choosing appropriate Christmas gifts for individuals with unique interests This metadata is provided to orient your extraction, not to constrain it. Extract entities and relationships freely from the document text itself; the metadata is descriptive context, not a checklist.",
|
|
"submit_elapsed_s": 6.6,
|
|
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