Triple
T37823579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pacinian corpuscle |
E942989
|
entity |
| Predicate | receptiveField |
P189250
|
FINISHED |
| Object | large receptive field |
—
|
LITERAL 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: large receptive field | Statement: [Pacinian corpuscle, receptiveField, large receptive field]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: receptiveField Context triple: [Pacinian corpuscle, receptiveField, large receptive field]
-
A.
visualField
Indicates the spatial region in which a visual system or observer can detect and perceive visual stimuli.
-
B.
areaOfRecruitment
Indicates the geographic or organizational scope from which candidates are sought or recruited for a position or opportunity.
-
C.
receptorInput
Indicates that a receptor receives or is provided with an input signal or stimulus from another entity.
-
D.
regionOfInterest
Indicates a specifically defined spatial or conceptual area that is selected for focused attention, analysis, or processing within a broader context.
-
E.
receptor
Indicates that one entity functions as a receptor for another, typically binding or receiving a signal, substance, or stimulus from it.
- F. None of above. chosen
Provenance (4 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_69f76ee987588190906506e759be5db3 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbbae559a8819086ef839973f8d9b2 |
completed | May 6, 2026, 10:04 p.m. |
| PD | Predicate disambiguation | batch_69fbb1440fa08190abf25ba684f75b6e |
completed | May 6, 2026, 9:23 p.m. |
| PDg | Predicate description generation | batch_69fbbae3fc508190adff3d7abbf107a4 |
completed | May 6, 2026, 10:04 p.m. |
Created at: May 3, 2026, 4:19 p.m.