Triple
T16067259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beverly Hills Fire Department |
E389764
|
entity |
| Predicate | hasTypeOfStation |
P27765
|
FINISHED |
| Object | fire station |
—
|
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: fire station | Statement: [Beverly Hills Fire Department, hasTypeOfStation, fire station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfStation Context triple: [Beverly Hills Fire Department, hasTypeOfStation, fire station]
-
A.
hasStationTypeAt
Indicates that a specific type of station is present or assigned at a particular location or point.
-
B.
stationType
chosen
Indicates the specific category or classification of a station based on its function, services, or operational characteristics.
-
C.
hasStationAt
Indicates that an entity maintains or operates a station located at a specified place.
-
D.
hasStationFunction
Indicates that an entity serves in a particular functional role or capacity at a station.
-
E.
hasBusStandType
Indicates the specific category or type of bus stand associated with a given bus stand entity.
- 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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1858a00888190b8505071575dc56f |
completed | April 17, 2026, 12:57 a.m. |
| PD | Predicate disambiguation | batch_69e18272f2288190a17d45fb01cc2b07 |
completed | April 17, 2026, 12:44 a.m. |
Created at: April 10, 2026, 4:57 a.m.