Triple
T23540825
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Cerone |
E577740
|
entity |
| Predicate | roleInDexter |
P152733
|
FINISHED |
| Object | writer |
—
|
LITERAL 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: writer | Statement: [Daniel Cerone, roleInDexter, writer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInDexter Context triple: [Daniel Cerone, roleInDexter, writer]
-
A.
roleInMysteryMen
Indicates that one entity has a specific role or part in the context of "Mystery Men," such as in its story, production, or related work.
-
B.
roleInWatchmen
Indicates that one entity has a specific role or function within the context of the work "Watchmen" in relation to the other entity.
-
C.
roleInCrime
Indicates the specific function, responsibility, or participation an entity has within the commission of a particular crime.
-
D.
roleInScoobyDoo
Indicates the specific function or character part an entity plays within the Scooby-Doo franchise or storyline.
-
E.
roleInDa5Bloods
Indicates that an entity had an acting role in the film "Da 5 Bloods."
- F. None of above. chosen
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_69e245f9d5d08190a4a20004e1784e20 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1ae1b3a8c8190b5b6a58f0476c5d2 |
completed | April 29, 2026, 7:07 a.m. |
| PD | Predicate disambiguation | batch_69f118afabd88190bd88f49597d120e8 |
completed | April 28, 2026, 8:29 p.m. |
| PDg | Predicate description generation | batch_69f121cc494081908c987adfcde89b0e |
completed | April 28, 2026, 9:08 p.m. |
Created at: April 17, 2026, 6:10 p.m.