Triple
T15008508
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | They Came Together |
E377772
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Paul Rudd |
E147055
|
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: Paul Rudd | Statement: [They Came Together, castMember, Paul Rudd]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paul Rudd Context triple: [They Came Together, castMember, Paul Rudd]
-
A.
Paul Rudd
chosen
Paul Rudd is an American actor and comedian best known for his charming, affable roles in films like Clueless, Anchorman, and as the title superhero in Marvel’s Ant-Man series.
-
B.
Luke Wilson
Luke Wilson is an American actor known for his roles in films such as "The Royal Tenenbaums," "Old School," and "Legally Blonde."
-
C.
Owen Wilson
Owen Wilson is an American actor and screenwriter known for his laid-back charm and roles in popular comedies and adventure films such as "Wedding Crashers," "Zoolander," and "Midnight in Paris."
-
D.
Mattias Ferrell
Mattias Ferrell is one of the sons of American actor and comedian Will Ferrell.
-
E.
Ryan Eggold
Ryan Eggold is an American actor best known for his roles on television series such as "The Blacklist" and "New Amsterdam," as well as in films like "BlacKkKlansman."
- 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_69d85cd3a3c881908c71fc424d459c17 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded73348d4819091d9e7f1b0fed822 |
completed | April 15, 2026, 12:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fed31c0ab08190814c41d852f9f523 |
completed | May 9, 2026, 6:24 a.m. |
Created at: April 10, 2026, 2:55 a.m.