Triple
T21397262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matt Bomer |
E527817
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Bomer |
—
|
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: Bomer | Statement: [Matt Bomer, familyName, Bomer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bomer Context triple: [Matt Bomer, familyName, Bomer]
-
A.
Bomer
chosen
Bomer is the surname of American actor Matt Bomer, known for his roles in television and film such as "White Collar" and "The Normal Heart."
-
B.
Bodmer
Bodmer is a surname most prominently associated with Sir Walter Bodmer, a British human geneticist known for his contributions to population genetics and cancer research.
-
C.
Bomowski
Bomowski is a surname most notably associated with the fictional character Tutty Bomowski.
-
D.
Bromfman
Bromfman is a surname most notably associated with Brazilian composer and music producer Pedro Bromfman, known for his work on film and television scores.
-
E.
Wonboyn
Wonboyn is a small coastal village in New South Wales, Australia, known for its lake, fishing, and proximity to national parks.
- 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_69e0b51ff3748190935c0a513c62a12b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b11a2aec8190a60e53b90d0823b1 |
completed | April 22, 2026, 11:29 a.m. |
Created at: April 16, 2026, 5:13 p.m.