Triple
T9859672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soarin' Over California |
E239676
|
entity |
| Predicate | numberOfTheaters |
P2427
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Soarin' Over California, numberOfTheaters, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTheaters Context triple: [Soarin' Over California, numberOfTheaters, 3]
-
A.
hasNumberOfTheatres
chosen
Indicates the quantity of theatres associated with or present in a given entity.
-
B.
hasNumberOfCinemas
Indicates the quantity of cinemas associated with a given entity.
-
C.
cinemaStatus
Indicates the current operational or functional state of a cinema, such as whether it is open, closed, or otherwise restricted.
-
D.
scopeOfTheatres
Indicates that there is a defined range, coverage, or extent of activities, responsibilities, or influence associated with particular theatres (e.g., theaters of operation or venues).
-
E.
workPremiereTheater
Indicates the theater where a work (such as a play, film, or performance) was first publicly premiered.
- 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_69ca84e6493081909cf58c8d42ea856b |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb39b06b48190ab53ff00ff0513ca |
completed | April 2, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69cd1d7621d48190aa6a6f34399514b0 |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:35 p.m.