Triple
T30996956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean Sorel |
E789829
|
entity |
| Predicate | hasNotableScreenPresence |
P43156
|
FINISHED |
| Object | leading man in European genre films |
—
|
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: leading man in European genre films | Statement: [Jean Sorel, hasNotableScreenPresence, leading man in European genre films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableScreenPresence Context triple: [Jean Sorel, hasNotableScreenPresence, leading man in European genre films]
-
A.
hasNotableShow
Indicates that an entity is associated with a particular show that is considered notable or significant.
-
B.
hasNotableFeature
Indicates that an entity possesses a specific characteristic, trait, or attribute that is considered significant or noteworthy.
-
C.
notablePresence
chosen
Indicates that an entity has a significant or prominent presence in relation to another entity, context, or domain.
-
D.
hasScreen
Indicates that an entity is equipped with or includes a screen or display component.
-
E.
hasNotableScript
Indicates that an entity is associated with a script (such as a writing system or screenplay) that is considered notable or significant.
- 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_69f224c65a348190baaed1c01a29900c |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a0075be7f54819081ab12bc1dab53bb |
completed | May 10, 2026, 12:10 p.m. |
| PD | Predicate disambiguation | batch_6a0073a19030819098c23faa3adcb96e |
completed | May 10, 2026, 12:01 p.m. |
Created at: April 29, 2026, 8:56 p.m.