Triple
T1733883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Office for Civil Rights (HHS) |
E37876
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | OCR |
E137600
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: OCR | Statement: [Office for Civil Rights (HHS), abbreviation, OCR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: OCR Context triple: [Office for Civil Rights (HHS), abbreviation, OCR]
-
A.
OCR
chosen
OCR is the Office for Civil Rights, a U.S. government agency responsible for enforcing civil rights laws and ensuring equal access and non-discrimination in federally funded programs.
-
B.
Kurzweil OCR (optical character recognition) systems
Kurzweil OCR (optical character recognition) systems are pioneering software tools that convert printed text into digital, machine-readable form, widely used for document digitization and accessibility for the visually impaired.
-
C.
HOCR
HOCR is the commonly used abbreviation for the Head of the Charles Regatta, a major annual rowing event held on the Charles River in Boston and Cambridge, Massachusetts.
-
D.
Gradient-based learning applied to document recognition
"Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
-
E.
CLIP
CLIP is an OpenAI model that learns joint representations of images and text, enabling tasks like zero-shot image classification and natural language-based image retrieval.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a8861cc6ac8190ac0b2e31ccf62851 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa63a0308c81909145dec7069bd06e |
completed | March 6, 2026, 5:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad8afd15a881909a5d55dd960799d1 |
completed | March 8, 2026, 2:43 p.m. |
Created at: March 4, 2026, 7:30 p.m.