Triple
T6629557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chris Tucker |
E149887
|
entity |
| Predicate | birthName |
P65
|
FINISHED |
| Object | Christopher Tucker |
E149887
|
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: Christopher Tucker | Statement: [Chris Tucker, birthName, Christopher Tucker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Christopher Tucker Context triple: [Chris Tucker, birthName, Christopher Tucker]
-
A.
Chris Tucker
chosen
Chris Tucker is an American actor and comedian best known for his high-energy performances in the Rush Hour film series and other popular comedies.
-
B.
Chris Kattan
Chris Kattan is an American comedian and actor best known for his work on "Saturday Night Live" and roles in films like "A Night at the Roxbury."
-
C.
Joe Lo Truglio
Joe Lo Truglio is an American actor and comedian best known for his role as the earnest, quirky detective Charles Boyle on the television sitcom "Brooklyn Nine-Nine."
-
D.
Anthony Anderson
Anthony Anderson is an American actor and comedian known for his roles in film and television, including the hit sitcom "Black-ish."
-
E.
David Koechner
David Koechner is an American character actor and comedian best known for his scene-stealing roles in films like Anchorman and the TV series The Office.
- 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_69c687ee50048190aa151765bef16193 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6afa5c9b48190b645be96d446d0ca |
completed | March 27, 2026, 4:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6eeeaa67881908bef71c5fa61c599 |
completed | March 27, 2026, 8:56 p.m. |
Created at: March 27, 2026, 1:59 p.m.