Triple
T20802666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Sidney Warner |
E512077
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Daniel Sidney Warner |
—
|
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: Daniel Sidney Warner | Statement: [Daniel Sidney Warner, fullName, Daniel Sidney Warner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Sidney Warner Context triple: [Daniel Sidney Warner, fullName, Daniel Sidney Warner]
-
A.
Daniel Sidney Warner
chosen
Daniel Sidney Warner was an American holiness preacher and reformer best known as a key early leader in the Church of God movement centered in Anderson, Indiana.
-
B.
Robert Warner
Robert Warner is an actor known for his role in the film "Deranged."
-
C.
Warren Wells
Warren Wells was an American professional football wide receiver best known for his standout seasons with the Oakland Raiders in the late 1960s.
-
D.
Sam Warner
Sam Warner was a co-founder and pioneering executive of Warner Bros. who helped usher in the era of sound films in Hollywood.
-
E.
Sam Warner
Sam Warner is a fictional character from the American television sitcom "Yes, Dear," which centers on the comedic challenges of family life and parenting.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4cc69f481908e98751e697b9df4 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2b207c48190a9ca5895bdf85245 |
completed | April 21, 2026, 12:20 a.m. |
Created at: April 16, 2026, 12:39 p.m.