Triple
T20269303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pantages vaudeville and motion picture chain |
E499049
|
entity |
| Predicate | peakNumberOfTheaters |
P2427
|
FINISHED |
| Object | dozens of venues across North America |
—
|
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: dozens of venues across North America | Statement: [Pantages vaudeville and motion picture chain, peakNumberOfTheaters, dozens of venues across North America]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: peakNumberOfTheaters Context triple: [Pantages vaudeville and motion picture chain, peakNumberOfTheaters, dozens of venues across North America]
-
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.
servedInTheatres
Indicates that a film or performance was publicly exhibited in movie theaters or similar cinema venues.
-
D.
intendedTheater
Indicates the theater or venue that an event, performance, or screening is planned or meant to take place in.
-
E.
openedAsTheater
Indicates that an entity began operation or was first established specifically as a theater.
- 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_69da6275fa6c8190952924930adee150 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e675dc8e708190b840d687f134c9e8 |
completed | April 20, 2026, 6:52 p.m. |
| PD | Predicate disambiguation | batch_69e55b1e5e1c8190ba8a5544b1db9e1d |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 11, 2026, 11:42 p.m.