Triple
T38364752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eyes Without a Face |
E892406
|
entity |
| Predicate | disfiguredCharacter |
P178191
|
FINISHED |
| Object | Christiané Génessier |
—
|
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: Christiané Génessier | Statement: [Eyes Without a Face, disfiguredCharacter, Christiané Génessier]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: disfiguredCharacter Context triple: [Eyes Without a Face, disfiguredCharacter, Christiané Génessier]
-
A.
hasDisfiguredHero
Indicates that a work features a hero character who is physically disfigured.
-
B.
faceDisfigurement
chosen
Indicates that an entity has a noticeable alteration or damage to the normal appearance or structure of its face.
-
C.
disfigurementCauseInStory
Indicates that one entity is the cause or source of another entity’s disfigurement within the context of a narrative or story.
-
D.
tormentsCharacter
Indicates that one entity causes ongoing psychological or physical suffering to another character.
-
E.
victimCharacter
Indicates that one entity is the victim or target of harm, wrongdoing, or an adverse action carried out by another entity.
- 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_69f76e47cb4c8190bdd92cd1db59c0c5 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fcc7a4d7f881908b43b960911b81e9 |
completed | May 7, 2026, 5:11 p.m. |
| PD | Predicate disambiguation | batch_69fcc589720c819089c8f500fea3c86a |
completed | May 7, 2026, 5:02 p.m. |
Created at: May 3, 2026, 4:31 p.m.