Triple
T3536253
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jennifer Aniston |
E74778
|
entity |
| Predicate | coStarredWith |
P14987
|
FINISHED |
| Object | Matt LeBlanc |
E325162
|
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: Matt LeBlanc | Statement: [Jennifer Aniston, coStarredWith, Matt LeBlanc]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matt LeBlanc Context triple: [Jennifer Aniston, coStarredWith, Matt LeBlanc]
-
A.
Matt LeBlanc
chosen
Matt LeBlanc is an American actor best known for playing the lovable, dim-witted Joey Tribbiani on the hit sitcom "Friends" and its spin-off "Joey."
-
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.
Mark Curtis
Mark Curtis is a British historian and author known for his critical works on UK foreign policy and Western interventionism.
-
E.
David Spade
David Spade is an American comedian and actor known for his work on "Saturday Night Live" and in numerous comedy films and television shows.
- 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_69ad85d1a3948190931fd1ea1f49717b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbcc7b92481908d2d99948780f4d0 |
completed | March 8, 2026, 6:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bb87c3748190bce62e86fcdfa380 |
completed | March 13, 2026, 7:23 a.m. |
Created at: March 8, 2026, 3:20 p.m.