Triple
T21719786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leo Parker |
E536124
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Leo Parker |
—
|
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: Leo Parker | Statement: [Leo Parker, name, Leo Parker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leo Parker Context triple: [Leo Parker, name, Leo Parker]
-
A.
Leo Parker
chosen
Leo Parker was an American baritone saxophonist known for his work in the bebop and hard bop jazz scenes of the 1940s and 1950s.
-
B.
Martin Henderson
Martin Henderson is a New Zealand actor known for his roles in films like "The Ring" and "Everest" and TV series such as "Grey's Anatomy" and "Virgin River."
-
C.
Christopher Henderson
Christopher Henderson is a fictional high-ranking counterterrorism operative and former mentor to Jack Bauer in the television series "24."
-
D.
Richard Gant
Richard Gant is an American character actor known for his roles in film and television, often portraying authoritative or tough-minded figures.
-
E.
Ian Blair
Ian Blair is a British crime writer best known for his detective novels and contributions to the mystery genre.
- 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_69e0c46c6dd88190a595375fa6ebd701 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69efd96de818819084c268d4775a8e3a |
completed | April 27, 2026, 9:47 p.m. |
Created at: April 16, 2026, 6:47 p.m.