Triple
T13258995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael Vaughn |
E315737
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Michael |
E21023
|
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: Michael | Statement: [Michael Vaughn, hasGivenName, Michael]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Context triple: [Michael Vaughn, hasGivenName, Michael]
-
A.
Michael
chosen
Michael is a common masculine given name of Hebrew origin meaning "Who is like God?"
-
B.
Michael
"Michael" is a 1996 fantasy-comedy film starring John Travolta as an unconventional archangel visiting Earth.
-
C.
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.
-
D.
Mike
Mike is the nickname of the fictional character Macaulay "Mike" Connor.
-
E.
Mike
Mike is the central con artist protagonist in David Mamet’s 1987 psychological thriller film "House of Games."
- 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_69d806b1d9ac8190852c5571d5bd5f0f |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98f778088819082b8a596c04bfe02 |
completed | April 11, 2026, 12:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f70a3d6b808190b4ae5225961de03f |
completed | May 3, 2026, 8:41 a.m. |
Created at: April 9, 2026, 9:25 p.m.