Triple
T7353579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | county of New Jersey |
E169564
|
entity |
| Predicate | hasTypicalSeat |
P48956
|
FINISHED |
| Object | county seat municipality |
—
|
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: county seat municipality | Statement: [county of New Jersey, hasTypicalSeat, county seat municipality]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalSeat Context triple: [county of New Jersey, hasTypicalSeat, county seat municipality]
-
A.
hasSeat
Indicates that one entity possesses, provides, or includes a seat for another entity.
-
B.
typicalSeat
chosen
Indicates the usual or standard seating position or location associated with an entity in a given context.
-
C.
hasSeatAt
Indicates that an entity occupies or holds a place, position, or membership within a specific group, body, or location.
-
D.
hadSeatType
Indicates that an entity was assigned or associated with a specific type or category of seat.
-
E.
hasSeating
Indicates that one entity provides or contains seating capacity or seating arrangements for another 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_69c68a5878888190968ce4d04db8d69f |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f139505c8190a7158cf59a6e089e |
completed | March 27, 2026, 9:06 p.m. |
| PD | Predicate disambiguation | batch_69c6f02aeeb8819099d1626566cec18b |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:05 p.m.