Triple
T939963
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chair P |
E20282
|
entity |
| Predicate | hasSeatCategory |
P9547
|
FINISHED |
| Object | capital-letter chair |
—
|
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: capital-letter chair | Statement: [Chair P, hasSeatCategory, capital-letter chair]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeatCategory Context triple: [Chair P, hasSeatCategory, capital-letter chair]
-
A.
seatCategory
chosen
Indicates the classification or type of a seat (e.g., by comfort level, price tier, or section) assigned to an entity.
-
B.
hasSeat
Indicates that one entity possesses, provides, or includes a seat 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.
hasSeating
Indicates that one entity provides or contains seating capacity or seating arrangements for another entity.
-
E.
hasReservedSeats
Indicates that specific seats have been set aside or allocated in advance for a particular entity or purpose.
- 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_69a493b0270c81909e6c9ce310f6aa55 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b38b7da08190ac0853655dab678a |
completed | March 1, 2026, 9:45 p.m. |
| PD | Predicate disambiguation | batch_69a4b29c68f48190aecad10e351a99de |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.