Triple
T1027650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chair U |
E22175
|
entity |
| Predicate | hasSeatType |
P9547
|
FINISHED |
| Object | individual membership seat |
—
|
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: individual membership seat | Statement: [Chair U, hasSeatType, individual membership seat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeatType Context triple: [Chair U, hasSeatType, individual membership seat]
-
A.
hasSeat
Indicates that one entity possesses, provides, or includes a seat for another entity.
-
B.
hasSeating
Indicates that one entity provides or contains seating capacity or seating arrangements for another entity.
-
C.
hasSeatAt
Indicates that an entity occupies or holds a place, position, or membership within a specific group, body, or location.
-
D.
hasClubSeats
Indicates that an entity (such as a venue or section) includes or is equipped with club-level seating.
-
E.
seatCategory
chosen
Indicates the classification or type of a seat (e.g., by comfort level, price tier, or section) assigned to an 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_69a493d6e380819097b384986ffc315c |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b95d35888190a20593a278175df7 |
completed | March 1, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69a4b7276180819085c6b23501a6a6e0 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:41 p.m.