Triple
T2310726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Contact Theatre |
E51949
|
entity |
| Predicate | numberOfAuditoria |
P37976
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Contact Theatre, numberOfAuditoria, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAuditoria Context triple: [Contact Theatre, numberOfAuditoria, 2]
-
A.
numberOfInvestigations
Indicates the count of investigations associated with or conducted by a given entity.
-
B.
hasAudienceSize
Indicates the relationship between an entity and the number of people or size of group that receives, views, or engages with it.
-
C.
numberOfEvents
Indicates the quantity or count of events associated with a given entity or context.
-
D.
numberOfCases
Indicates the total count of individual instances, occurrences, or records associated with a particular situation, condition, or category.
-
E.
numberOfInstances
Indicates the quantity or count of distinct occurrences or instances associated with a given entity or context.
- 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_69a88b0bb30c81908ded03b006d29387 |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abc685f05481909c863b29d1f6bacd |
completed | March 7, 2026, 6:32 a.m. |
| PD | Predicate disambiguation | batch_69abc58e88e481908733fdf79d3f8a15 |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abc682d094819081a96ffb77c4c42a |
completed | March 7, 2026, 6:32 a.m. |
Created at: March 4, 2026, 7:49 p.m.