Triple
T35399125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Bear and the Doll |
E1023170
|
entity |
| Predicate | featuresBrigitteBardotAs |
P182870
|
FINISHED |
| Object | temperamental socialite |
—
|
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: temperamental socialite | Statement: [The Bear and the Doll, featuresBrigitteBardotAs, temperamental socialite]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresBrigitteBardotAs Context triple: [The Bear and the Doll, featuresBrigitteBardotAs, temperamental socialite]
-
A.
hasBrigitteBardotRoleType
chosen
Indicates that an entity has a role type specifically associated with Brigitte Bardot (e.g., portraying her or a role category defined in relation to her).
-
B.
characterPortrayedBySandrineBonnaire
Indicates that a character is portrayed or played by the actress Sandrine Bonnaire.
-
C.
characterPlayedBy_Danielle Darrieux
Indicates that a given character is portrayed or acted by Danielle Darrieux.
-
D.
brandKnownFor
Indicates that a brand is widely recognized or associated with a particular product, service, quality, or characteristic.
-
E.
portrayerKnownFor
Indicates that a person is especially recognized or famous for portraying a particular role, character, or work.
- 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_69f76df43ca4819098711ca4370f1bb9 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037c8d06cc8190ab6a5e18d9d2571e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0324d08190ac5b610cc0f6a38c |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:03 p.m.