Triple
T13767851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan O'Brien |
E330796
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Dan O'Brien |
E330796
|
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: Dan O'Brien | Statement: [Dan O'Brien, name, Dan O'Brien]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan O'Brien Context triple: [Dan O'Brien, name, Dan O'Brien]
-
A.
Dan O'Brien
chosen
Dan O'Brien is a former American decathlete and Olympic gold medalist widely regarded as one of the greatest decathletes in history.
-
B.
Steve O'Brien
Steve O'Brien is an individual known primarily as the sibling of Jack O'Brien.
-
C.
Matt O’Dowd
Matt O’Dowd is an astrophysicist and science communicator known for explaining complex cosmology and physics concepts to the public, particularly through online video series.
-
D.
Nick O'Brien
Nick O'Brien is a hard-edged, morally ambiguous Los Angeles County Sheriff's detective who leads an elite unit in the crime thriller film "Den of Thieves."
-
E.
Graham O'Brien
Graham O'Brien is a companion of the Thirteenth Doctor in the long-running British science fiction television series Doctor Who.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0227f2c48190983ccc9395e4e7a2 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fea59d27bc81908ace0b7db9f57215 |
completed | May 9, 2026, 3:10 a.m. |
Created at: April 9, 2026, 10:10 p.m.