Triple
T3759761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hail, Caesar! |
E82131
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Jonah Hill |
E151010
|
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: Jonah Hill | Statement: [Hail, Caesar!, portrayedBy, Jonah Hill]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jonah Hill Context triple: [Hail, Caesar!, portrayedBy, Jonah Hill]
-
A.
Jonah Hill
chosen
Jonah Hill is an American actor, comedian, and filmmaker known for his roles in films such as Superbad, Moneyball, and The Wolf of Wall Street.
-
B.
Seth Rogen
Seth Rogen is a Canadian actor, comedian, writer, producer, and director known for his distinctive laugh and roles in numerous hit comedy films such as "Superbad," "Pineapple Express," and "Knocked Up."
-
C.
Zach Woods
Zach Woods is an American actor and comedian best known for his roles on television series such as "The Office," "Silicon Valley," and "Avenue 5."
-
D.
Jason Segel
Jason Segel is an American actor, comedian, screenwriter, and producer best known for his roles in the sitcom "How I Met Your Mother" and films like "Forgetting Sarah Marshall."
-
E.
Luke Wilson
Luke Wilson is an American actor known for his roles in films such as "The Royal Tenenbaums," "Old School," and "Legally Blonde."
- 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_69ad8b1db40081908b61ffa6b78afd4d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcbc3d3f48190974cec104080949f |
completed | March 8, 2026, 7:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4f0321c4c8190ba372148c483c4ca |
completed | March 14, 2026, 5:20 a.m. |
Created at: March 8, 2026, 3:35 p.m.