Triple
T18205023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LayoutLM |
E435880
|
entity |
| Predicate | hasVersion |
P455
|
FINISHED |
| Object | LayoutLMv3 |
—
|
NE NERFINISHED |
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: LayoutLMv3 | Statement: [LayoutLM, hasVersion, LayoutLMv3]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LayoutLMv3 Context triple: [LayoutLM, hasVersion, LayoutLMv3]
-
A.
LayoutLM
chosen
LayoutLM is a transformer-based document understanding model that jointly leverages text, layout, and visual information to process and analyze scanned documents and forms.
-
B.
Azure Form Recognizer
Azure Form Recognizer is a cloud-based AI service that uses machine learning to extract structured data, text, and key-value pairs from documents and forms.
-
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.
Office Lens
Office Lens is a Microsoft mobile scanning app that captures documents, whiteboards, and other content with a device camera and converts them into editable, shareable digital files.
-
E.
OCR
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e222831081908f7d5500424e3acb |
completed | April 19, 2026, 2:09 p.m. |
Created at: April 10, 2026, 10:32 a.m.