Triple
T83459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bridgewater Hall |
E1677
|
entity |
| Predicate | hasBoxOffice |
P3427
|
FINISHED |
| Object | on-site ticket office |
—
|
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: on-site ticket office | Statement: [Bridgewater Hall, hasBoxOffice, on-site ticket office]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBoxOffice Context triple: [Bridgewater Hall, hasBoxOffice, on-site ticket office]
-
A.
boxOfficeGrossUSD
Indicates the total amount of money an entity earned at the box office, expressed in U.S. dollars.
-
B.
hasNumberOfTheatres
Indicates the quantity of theatres associated with or present in a given entity.
-
C.
hasNumberOfCinemas
Indicates the quantity of cinemas associated with a given entity.
-
D.
hasAcademy
Indicates that an entity possesses, operates, or is formally associated with an academy as part of its structure or offerings.
-
E.
hasSequel
Indicates that one work is followed by another work that continues its story, timeline, or thematic development.
- F. None of above. chosen
Provenance (4 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_69a24c8150408190910a693eb51c1f71 |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a24f4ccb5081908decac81f4af01bf |
completed | Feb. 28, 2026, 2:13 a.m. |
| PD | Predicate disambiguation | batch_69a24eb469548190b38c24e81f36c838 |
completed | Feb. 28, 2026, 2:11 a.m. |
| PDg | Predicate description generation | batch_69a24f4b4658819087902414959161fb |
completed | Feb. 28, 2026, 2:13 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.