Triple
T22101827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sapphire |
E546189
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Michael Craig |
—
|
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: Michael Craig | Statement: [Sapphire, stars, Michael Craig]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Craig Context triple: [Sapphire, stars, Michael Craig]
-
A.
Michael Craig
chosen
Michael Craig is a British actor and screenwriter known for his work in mid-20th-century film and television, including roles in dramas and thrillers.
-
B.
Michael Pennington
Michael Pennington is a distinguished English actor and director, particularly renowned for his work in classical theatre and Shakespearean performance.
-
C.
John Gibbon
John Gibbon was a 19th-century United States Army officer and Civil War general who later played a key role in the Indian Wars, including campaigns against the Nez Perce.
-
D.
Michael Pate
Michael Pate was an Australian actor and writer known for his prolific character roles in film and television from the 1940s through the 1980s.
-
E.
David Leahy
David Leahy is a notable individual recognized for achievements significant enough to be associated with the surname Leahy.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69e11e378dc08190896d6a51597afd5a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129163b908190b63ace06016f4db8 |
completed | April 28, 2026, 9:39 p.m. |
Created at: April 16, 2026, 8:30 p.m.