Triple
T10905805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Good Place |
E257565
|
entity |
| Predicate | mainCharacter |
P1183
|
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: [The Good Place, mainCharacter, Michael]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Context triple: [The Good Place, mainCharacter, Michael]
-
A.
Michael
"Michael" is a 1996 fantasy-comedy film starring John Travolta as an unconventional archangel visiting Earth.
-
B.
Michael
chosen
Michael is a common masculine given name of Hebrew origin meaning "Who is like God?"
-
C.
Mike
Mike is a character in Terrence McNally’s play "The Lisbon Traviata," which explores themes of friendship, obsession, and gay relationships.
-
D.
Kevin
Kevin is a silent, cannibalistic serial killer and devoutly religious assassin from Frank Miller's Sin City graphic novel series.
-
E.
Kevin
Kevin is the given name of Kevin Garnett, a Hall of Fame American professional basketball player known for his intensity, versatility, and NBA championship with the Boston Celtics.
- 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_69d6aa8550c8819095508a2ed9acf3db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7706679b48190a1f29fc64fe8a334 |
completed | April 9, 2026, 9:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e155454890819087028a72ac2be8de |
completed | April 16, 2026, 9:31 p.m. |
Created at: April 8, 2026, 9:22 p.m.