Triple
T32745112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | L’Amour |
E837332
|
entity |
| Predicate | hasCentralCharacterPortrayedBy |
P54972
|
FINISHED |
| Object | Anne Wiazemsky |
—
|
NE NERFINISHED |
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: Anne Wiazemsky | Statement: [L’Amour, hasCentralCharacterPortrayedBy, Anne Wiazemsky]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCentralCharacterPortrayedBy Context triple: [L’Amour, hasCentralCharacterPortrayedBy, Anne Wiazemsky]
-
A.
hasMainCharacterFrom
Indicates that a work of fiction has a main character who originates from or belongs to a specified place, group, or source.
-
B.
characterPortrayedIs
chosen
Indicates that one entity serves as the fictional or dramatic role that is depicted or played by another entity.
-
C.
laterMainCharacterOf
Indicates that one entity becomes the main character of a work at a later point in time, succeeding another main character.
-
D.
sonCharacterPortrayedBy
Indicates that a person is the actor who portrays a specific son character in a work of fiction.
-
E.
characterInFocus
Indicates that a particular character is the primary subject or focal point within a given context, scene, or narrative segment.
- 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_69f34936e1748190b797e406e4e9293a |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f7b0e5744c8190a22c1e1d6fcfa466 |
completed | May 3, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f7ab70d034819080295628497d8582 |
completed | May 3, 2026, 8:09 p.m. |
Created at: May 1, 2026, 1:12 a.m.