Triple
T38364754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eyes Without a Face |
E892406
|
entity |
| Predicate | assistantCharacter |
P142131
|
FINISHED |
| Object | Louise |
—
|
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: Louise | Statement: [Eyes Without a Face, assistantCharacter, Louise]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: assistantCharacter Context triple: [Eyes Without a Face, assistantCharacter, Louise]
-
A.
semiAutobiographicalCharacter
Indicates that a character is based partly on the real-life experiences, personality, or identity of its creator or author, but is not a fully direct self-portrayal.
-
B.
exampleCharacter
Indicates that one entity is an illustrative or sample character associated with another entity, typically used for demonstration or example purposes.
-
C.
aiCharacter
Indicates that an entity is a character or agent whose behavior or role is driven by artificial intelligence.
-
D.
fictionalCharacterAssisted
chosen
Indicates that one fictional character provided help, support, or assistance to another fictional character.
-
E.
mentorCharacter
Indicates that one character serves as a mentor, providing guidance, teaching, or support to another character.
- 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.