Triple
T2219396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Le Rêve – The Dream |
E48104
|
entity |
| Predicate | theaterConfiguration |
P28912
|
FINISHED |
| Object | theater-in-the-round |
—
|
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: theater-in-the-round | Statement: [Le Rêve – The Dream, theaterConfiguration, theater-in-the-round]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: theaterConfiguration Context triple: [Le Rêve – The Dream, theaterConfiguration, theater-in-the-round]
-
A.
filmSettingTheater
Indicates that a film’s setting or key scenes take place in a theater (such as a cinema or playhouse).
-
B.
theaterCommander
Indicates that an entity serves as the commanding authority over military operations within a specific theater or area of operations.
-
C.
theaterType
chosen
Indicates the specific kind or category of theater associated with an entity (e.g., cinema, opera house, drama theater).
-
D.
theatricalSetting
Indicates the spatial or contextual environment in which a theatrical performance or dramatic action takes place.
-
E.
appliesToTheater
Indicates that something is relevant or applicable specifically to a theater or theatrical context.
- 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_69a88aa1ee708190862c8c378c41e9eb |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc011d50c8190b1c375cc633f8189 |
completed | March 7, 2026, 6:05 a.m. |
| PD | Predicate disambiguation | batch_69abbdac31d8819092d17815e11921e9 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:46 p.m.