Triple
T32200436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | English Theatre Company in Paris |
E822516
|
entity |
| Predicate | audienceLanguageContext |
P94660
|
FINISHED |
| Object | French-speaking audiences |
—
|
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: French-speaking audiences | Statement: [English Theatre Company in Paris, audienceLanguageContext, French-speaking audiences]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: audienceLanguageContext Context triple: [English Theatre Company in Paris, audienceLanguageContext, French-speaking audiences]
-
A.
languageOfSurroundingCulture
Indicates that one entity is the language predominantly used or characteristic of the surrounding culture associated with another entity.
-
B.
hasTargetAudienceLanguage
chosen
Indicates that something is intended for or directed toward an audience that speaks a particular language.
-
C.
hostCountryLanguageContext
Indicates the linguistic environment or language norms present in the country where an entity is hosted or operating.
-
D.
usesLanguageFor
Indicates that an entity employs a particular language as a tool or medium to perform some activity, function, or purpose.
-
E.
languageEmphasizes
Indicates that one language or linguistic system places particular focus, importance, or prominence on a specific feature, concept, or element compared to others.
- 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_69f349093174819086e633c190a51aa8 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6bb3bcbe88190ab5511323b95e6b7 |
completed | May 3, 2026, 3:04 a.m. |
| PD | Predicate disambiguation | batch_69f6b6293188819080d5041ca0adb969 |
completed | May 3, 2026, 2:42 a.m. |
Created at: May 1, 2026, 12:36 a.m.