Triple
T15429663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mike Mitchell |
E369603
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Mike |
unclear NED1
|
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: Mike | Statement: [Mike Mitchell, givenName, Mike]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mike Context triple: [Mike Mitchell, givenName, Mike]
-
A.
Mike
Mike is a character in Terrence McNally’s play "The Lisbon Traviata," which explores themes of friendship, obsession, and gay relationships.
-
B.
Mike
Mike is the young boy protagonist of the 1992 family adventure film "Radio Flyer," which centers on his imaginative efforts to escape a troubled home life with his brother.
-
C.
Mike
Mike is the central con artist protagonist in David Mamet’s 1987 psychological thriller film "House of Games."
-
D.
Mike
Mike is the nickname of the fictional character Macaulay "Mike" Connor.
-
E.
Mike
Mike is the given name of Lt. Mike Stone, a fictional San Francisco homicide detective from the television series "The Streets of San Francisco."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69d85a1849f48190bf898068b2806fae |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ed8ea888190bff8dc14859cca31 |
completed | April 16, 2026, 1:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff1a827d9081909fabc48bc685ba5b |
completed | May 9, 2026, 11:29 a.m. |
Created at: April 10, 2026, 3:21 a.m.