Triple
T18205032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LayoutLM |
E435880
|
entity |
| Predicate | paperTitle |
P38
|
FINISHED |
| Object | LayoutLM: Pre-training of Text and Layout for Document Image Understanding |
—
|
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: LayoutLM: Pre-training of Text and Layout for Document Image Understanding | Statement: [LayoutLM, paperTitle, LayoutLM: Pre-training of Text and Layout for Document Image Understanding]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LayoutLM: Pre-training of Text and Layout for Document Image Understanding Context triple: [LayoutLM, paperTitle, LayoutLM: Pre-training of Text and Layout for Document Image Understanding]
-
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.
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.
Longformer
Longformer is a transformer-based neural network architecture designed for efficient processing of very long sequences using sparse attention mechanisms.
-
D.
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.
-
E.
Embeddings from Language Models
Embeddings from Language Models (ELMo) is a deep contextual word representation technique that uses bidirectional language models to capture rich, context-dependent meanings of words for natural language processing tasks.
- 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.