Triple
T33310289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shirley Valentine |
E852856
|
entity |
| Predicate | hasMainCharacterNationality |
P15237
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Shirley Valentine, hasMainCharacterNationality, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMainCharacterNationality Context triple: [Shirley Valentine, hasMainCharacterNationality, English]
-
A.
hasLeadActorNationality
Indicates that the nationality of the lead actor in a work is a specified country or nationality.
-
B.
hasCastMemberNationality
Indicates that at least one cast member of a work has the specified nationality.
-
C.
hasDirectorNationality
Indicates that the nationality of a director is associated with a given entity (such as a film, organization, or work).
-
D.
nationalityInStory
chosen
Indicates that a character or entity in a narrative is associated with a particular nationality within the context of that story.
-
E.
hasOwnerNationalityStereotype
Indicates that an entity is associated with a stereotype about the nationality of its owner.
- 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_69f349679fd8819093b9b40e989440e3 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a00d24043e8819090cc473b6c0923d0 |
completed | May 10, 2026, 6:45 p.m. |
| PD | Predicate disambiguation | batch_6a00d1ec12fc81908c514ed088ef8300 |
completed | May 10, 2026, 6:43 p.m. |
Created at: May 1, 2026, 1:33 a.m.