Triple
T10512555
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Return to Oz (1985 film) |
E247950
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Matt Clark |
E513657
|
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: Matt Clark | Statement: [Return to Oz (1985 film), stars, Matt Clark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matt Clark Context triple: [Return to Oz (1985 film), stars, Matt Clark]
-
A.
Matt Clark
chosen
Matt Clark was an American character actor known for his numerous supporting roles in Westerns and other films and television series from the 1960s onward.
-
B.
Dane Clark
Dane Clark was an American film and television actor known for his tough, working-class persona in numerous 1940s and 1950s Hollywood dramas and war movies.
-
C.
Mike E. Clark
Mike E. Clark is an American record producer best known for his long-running work with Insane Clown Posse and other artists on the Psychopathic Records label.
-
D.
Les Clark
Les Clark was an American animator and one of Disney’s famed "Nine Old Men," known for his influential work on many classic Disney films.
-
E.
Tim Clark
Tim Clark is a British airline executive best known as the longtime president of Emirates, where he played a key role in transforming it into a major global carrier.
- 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_69d381c4aa948190942e1d803143fb0e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d509ca214481909b3ed9265e7a6704 |
completed | April 7, 2026, 1:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d8dcf65f808190993dbacde2df20eb |
completed | April 10, 2026, 11:20 a.m. |
Created at: April 6, 2026, 12:27 p.m.