Triple
T37823663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | corpuscles of Pacini |
E942991
|
entity |
| Predicate | hasReceptiveField |
P189250
|
FINISHED |
| Object | large |
—
|
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 | Statement: [corpuscles of Pacini, hasReceptiveField, large]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReceptiveField Context triple: [corpuscles of Pacini, hasReceptiveField, large]
-
A.
receptiveField
chosen
Indicates the region or set of inputs in the source space (e.g., sensory surface or input layer) to which a given unit, neuron, or detector is responsive.
-
B.
hasPerceivingFunction
Indicates that an entity possesses a function or capability specifically for perceiving or sensing other entities, events, or stimuli.
-
C.
hasObservationArea
Indicates that an entity possesses or includes a designated area from which observations or monitoring activities are conducted.
-
D.
hasFieldOfView
Indicates that one entity possesses a visual coverage area within which it can perceive or detect other entities or regions.
-
E.
hasNeuralNetwork
Indicates that an entity possesses, incorporates, or is equipped with a neural network.
- F. None of above.
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_69f76ee987588190906506e759be5db3 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1772e48190ba738c6d11b321e2 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:19 p.m.