Triple
T32469266
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daredevil (2003 film score) |
E829801
|
entity |
| Predicate | subjectHasFictionalCharacter |
P50141
|
FINISHED |
| Object | Daredevil |
—
|
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: Daredevil | Statement: [Daredevil (2003 film score), subjectHasFictionalCharacter, Daredevil]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectHasFictionalCharacter Context triple: [Daredevil (2003 film score), subjectHasFictionalCharacter, Daredevil]
-
A.
fictionalCharacterAssociatedWith
Indicates that there is a notable connection or association between a fictional character and another entity, such as a work, creator, or universe.
-
B.
hasFictionalWork
Indicates that one entity is the creator, owner, or source of a fictional work associated with another entity.
-
C.
isFictionalPersonFrom
Indicates that a fictional person originates from or is associated with a particular place or source.
-
D.
isFictionalCharacter
Indicates that the subject is a character that exists only in fiction rather than in real life.
-
E.
fictionalCharacter
chosen
Indicates that one entity is a fictional character that appears within the narrative world of another entity (such as a work, series, or franchise).
- 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_69f3491ee87c81908cbf5890079c2af6 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fcc7338120819081cb46547d60f2cb |
completed | May 7, 2026, 5:09 p.m. |
| PD | Predicate disambiguation | batch_69fcc58566a0819082d5ea36e03bf0c6 |
completed | May 7, 2026, 5:01 p.m. |
Created at: May 1, 2026, 12:57 a.m.