Triple
T17793590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 99th Precinct |
E444229
|
entity |
| Predicate | hasRecurringSetArea |
P128941
|
FINISHED |
| Object | bullpen |
—
|
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: bullpen | Statement: [99th Precinct, hasRecurringSetArea, bullpen]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRecurringSetArea Context triple: [99th Precinct, hasRecurringSetArea, bullpen]
-
A.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
B.
hasAreaTotal
Indicates the total surface area associated with an entity, typically measured over its entire extent.
-
C.
hasAreaNumber
Indicates that an entity is associated with a specific area identified by a numerical code.
-
D.
hasAreaRange
Indicates that something’s area falls within a specified minimum-to-maximum range.
-
E.
hasCollectionArea
Indicates that an entity is associated with a specific geographic or spatial area from which items, specimens, or data are collected.
- 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_69d8b9efe370819095cd219b143ae727 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e487993a6c8190805e06d93dfc0dce |
completed | April 19, 2026, 7:43 a.m. |
| PD | Predicate disambiguation | batch_69e3d8d8e538819084f1584426b41d5e |
completed | April 18, 2026, 7:17 p.m. |
| PDg | Predicate description generation | batch_69e3db7704588190a34a422421152173 |
completed | April 18, 2026, 7:28 p.m. |
Created at: April 10, 2026, 10:13 a.m.