Triple
T3490442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Curtis Stigers |
E73716
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Curtis Stigers |
E73716
|
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: Curtis Stigers | Statement: [Curtis Stigers, name, Curtis Stigers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Curtis Stigers Context triple: [Curtis Stigers, name, Curtis Stigers]
-
A.
Curtis Stigers
chosen
Curtis Stigers is an American jazz and soul-influenced singer, saxophonist, and songwriter known for his early 1990s pop hits and later critically acclaimed jazz recordings.
-
B.
Robert Slaughter
Robert Slaughter was the husband of longtime U.S. Representative Louise Slaughter.
-
C.
John Stanier
John Stanier is a cinematographer best known for his work on major action films such as "Rambo III."
-
D.
Mick Rogers
Mick Rogers is an Australian former professional road cyclist known for his time-trialling strength and multiple world championship titles in the team time trial.
-
E.
Mike Starr
Mike Starr is an American character actor known for his imposing presence and frequent roles as tough guys or mobsters in films and television.
- 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_69ad85cca8d4819088494e9f3340fab5 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbb94190c8190a81eb41042e51a00 |
completed | March 8, 2026, 6:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b373bb0e00819087899a394f50295d |
completed | March 13, 2026, 2:17 a.m. |
Created at: March 8, 2026, 3:18 p.m.