Triple
T9280430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Craig Kilborn |
E223052
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Craig Kilborn |
E223052
|
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: Craig Kilborn | Statement: [Craig Kilborn, name, Craig Kilborn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Craig Kilborn Context triple: [Craig Kilborn, name, Craig Kilborn]
-
A.
Craig Kilborn
chosen
Craig Kilborn is an American television host, comedian, and actor best known for his early hosting roles on ESPN’s SportsCenter, The Daily Show, and The Late Late Show.
-
B.
Bill Engvall
Bill Engvall is an American stand-up comedian and actor best known as a member of the Blue Collar Comedy Tour and for his signature "Here's Your Sign" routine.
-
C.
Paul F. Tompkins
Paul F. Tompkins is an American comedian, actor, and writer known for his stand-up, podcast appearances, and character roles in television and film.
-
D.
Kevin Maher
Kevin Maher is an English former professional footballer and current football manager best known for his long association with Southend United.
-
E.
Dane Cook
Dane Cook is an American stand-up comedian and actor known for his energetic, observational comedy and roles in films such as "Good Luck Chuck" and "Employee of the Month."
- 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_69ca842123588190b3f2e1a69037d141 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd07cd9a1c8190af0521baa428ce10 |
completed | April 1, 2026, 11:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d09c52e2608190bb92d65785f51205 |
completed | April 4, 2026, 5:06 a.m. |
Created at: March 30, 2026, 7:34 p.m.