Triple
T23540469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alma |
E577730
|
entity |
| Predicate | collaboratesWith |
P37
|
FINISHED |
| Object | Mike |
—
|
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: Mike | Statement: [Alma, collaboratesWith, Mike]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mike Context triple: [Alma, collaboratesWith, Mike]
-
A.
Mike
Mike is the nickname of the fictional character Macaulay "Mike" Connor.
-
B.
Mike
Mike Gascoyne is a British motorsport engineer best known for his senior technical and design roles with several Formula One teams.
-
C.
Mike
Mike is the given name of American folk musician and folklorist Mike Seeger, known for his work in preserving traditional American music.
-
D.
Mike
Mike is the given name of American actor Mike Kellin, known for his character roles in film, television, and theater during the mid-20th century.
-
E.
Mike
Mike is the commonly used first name of Mike D’Antoni, an American professional basketball coach and former player known for his innovative, fast-paced offensive systems in the NBA.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69e245f9d5d08190a4a20004e1784e20 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1ae1b3a8c8190b5b6a58f0476c5d2 |
completed | April 29, 2026, 7:07 a.m. |
Created at: April 17, 2026, 6:10 p.m.