Triple
T26878680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | McCallum Theatre |
E676825
|
entity |
| Predicate | hasBoxOfficePhone |
P172345
|
FINISHED |
| Object | 760-340-2787 |
—
|
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: 760-340-2787 | Statement: [McCallum Theatre, hasBoxOfficePhone, 760-340-2787]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBoxOfficePhone Context triple: [McCallum Theatre, hasBoxOfficePhone, 760-340-2787]
-
A.
hasBoxOffice
Indicates that an entity (typically a film or performance) has a specific box office revenue amount or record associated with it.
-
B.
hasBoxOfficeType
Indicates the classification of a work’s box office performance or revenue category (e.g., type or scale of its box office results).
-
C.
officeNumber
Indicates the specific room or suite number assigned to an office within a building or complex.
-
D.
hasCallCenter
Indicates that an entity operates, is associated with, or is served by a call center.
-
E.
hasTelephoneService
Indicates that a subject is provided with or connected to telephone service.
- 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_69eee9bb44988190b6e11652d028bc59 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f6abe15d5c81909ccf4ce37f78bc43 |
completed | May 3, 2026, 1:58 a.m. |
| PD | Predicate disambiguation | batch_69f6aa1c555081908787dbf76147f180 |
completed | May 3, 2026, 1:51 a.m. |
| PDg | Predicate description generation | batch_69f6aaf31a548190b2f792ff4b8c002a |
completed | May 3, 2026, 1:54 a.m. |
Created at: April 27, 2026, 5:37 a.m.