Triple
T37790674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucie de Mirecourt |
E942076
|
entity |
| Predicate | hasCharacterOriginWork |
P34184
|
FINISHED |
| Object | French cinema |
—
|
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 cinema | Statement: [Lucie de Mirecourt, hasCharacterOriginWork, French cinema]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCharacterOriginWork Context triple: [Lucie de Mirecourt, hasCharacterOriginWork, French cinema]
-
A.
hasMultipleCharacterOrigins
Indicates that an entity is associated with more than one origin story or background for a character.
-
B.
hasOriginalCharacter
Indicates that an entity includes, features, or is associated with an original character distinct from pre-existing or canonical characters.
-
C.
basedOnCharacterOrigin
Indicates that one entity is derived from, inspired by, or determined according to the origin or background of a character.
-
D.
characterOrigin
chosen
Indicates the source, background, or initial context from which a character originates.
-
E.
originOfCharacter
Indicates the source or place from which a character originates or is created.
- 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_69f76ee5cb0c81909a363d1c929156c0 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69ff45793d5c81909dc503ad1f714ee2 |
completed | May 9, 2026, 2:32 p.m. |
| PD | Predicate disambiguation | batch_69ff41cb0e088190a6e9b03cb20e5fad |
completed | May 9, 2026, 2:16 p.m. |
Created at: May 3, 2026, 4:19 p.m.