Triple
T38596612
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | City of Angels (stage production) |
E934096
|
entity |
| Predicate | realWorldProtagonist |
P32529
|
FINISHED |
| Object | Stine |
E1416710
|
NE 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: Stine | Statement: [City of Angels (stage production), realWorldProtagonist, Stine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: realWorldProtagonist Context triple: [City of Angels (stage production), realWorldProtagonist, Stine]
-
A.
protagonistRealWorldName
Indicates that a character’s name in the real world (outside a fictional, virtual, or alternate setting) is associated with that character as the story’s protagonist.
-
B.
mainProtagonist
Indicates that the subject is the central character or primary focus in the narrative of the related work.
-
C.
protagonistType
Indicates the role or category that the main character (protagonist) of a story or scenario belongs to.
-
D.
characterRealWorldCounterpart
Indicates that a fictional character is based on, inspired by, or directly corresponds to a specific real-world person.
-
E.
protagonistIs
chosen
Indicates that one entity serves as the main character or central figure in relation to another entity or narrative context.
- F. None of above.
Provenance (4 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_69f76ecc17688190b389b693a5927501 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcdb0de8c08190928cd1323f80ab5c |
completed | May 7, 2026, 6:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a41eaa230748190b011df5db0c12875 |
completed | June 29, 2026, 3:46 a.m. |
| PD | Predicate disambiguation | batch_69fcd9017dd88190b32a73fe78909740 |
completed | May 7, 2026, 6:25 p.m. |
Created at: May 3, 2026, 4:32 p.m.