Triple
T8566798
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hildy |
E202823
|
entity |
| Predicate | hasProfessionLocation |
P69710
|
FINISHED |
| Object | New York City taxi service |
—
|
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: New York City taxi service | Statement: [Hildy, hasProfessionLocation, New York City taxi service]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionLocation Context triple: [Hildy, hasProfessionLocation, New York City taxi service]
-
A.
hasWorksLocatedIn
Indicates that the works or creations associated with an entity are situated or stored in a specified location.
-
B.
hasLocationRole
chosen
Indicates that an entity holds or plays a specific role in relation to a particular location (e.g., origin, destination, storage site, or operational area).
-
C.
hasLocationCity
Indicates that an entity is situated in, occurs in, or is associated with a specific city as its location.
-
D.
isAssociatedWithProfessionOfBearer
Indicates that one entity is connected to, or involved with, the profession or occupational role held by another entity.
-
E.
residencyLocation
Indicates the place where an entity lives or maintains its primary residence.
- 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_69ca8327b0a881908606ff860713964d |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe9d467c08190b2014d71ebbf8bbc |
completed | March 31, 2026, 3:35 p.m. |
| PD | Predicate disambiguation | batch_69cbd11856048190a1ce4b83a38f6965 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:20 p.m.