Triple
T18324117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daken |
E438958
|
entity |
| Predicate | creators |
P7732
|
FINISHED |
| Object | Steve Dillon |
—
|
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: Steve Dillon | Statement: [Daken, creators, Steve Dillon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steve Dillon Context triple: [Daken, creators, Steve Dillon]
-
A.
Steve Dillon
chosen
Steve Dillon was a British comic book artist best known for co-creating and illustrating the acclaimed series "Preacher."
-
B.
Alan Dillon
Alan Dillon is an Irish Fine Gael politician and former Gaelic footballer who serves as a Teachta Dála (TD) in the national parliament.
-
C.
Sean Dillon
Sean Dillon is a former IRA enforcer turned covert operative who serves as the central anti-hero in Jack Higgins' popular thriller novel series.
-
D.
P.J. Dillon
P.J. Dillon is an Irish cinematographer and screenwriter known for his work on films such as the historical drama "Black 47."
-
E.
Daniel Dillon
Daniel Dillon is a central character in Thomas Hardy’s novel "The Mayor of Casterbridge," known for his complex moral struggles and tragic personal downfall.
- 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_69d8b916a2d081909e249e4902f6aad9 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e50aa93fb0819083293b80e8400c4e |
completed | April 19, 2026, 5:02 p.m. |
Created at: April 10, 2026, 10:36 a.m.