Triple
T22803633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tag (2018 film) |
E564469
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Ed Helms |
—
|
NE NERFINISHED |
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: Ed Helms | Statement: [Tag (2018 film), starring, Ed Helms]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ed Helms Context triple: [Tag (2018 film), starring, Ed Helms]
-
A.
Ed Helms
chosen
Ed Helms is an American actor and comedian best known for his roles in the TV series "The Office" and "The Daily Show," as well as "The Hangover" film trilogy.
-
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.
Michael Urie
Michael Urie is an American actor best known for his role as Marc St. James on the television series "Ugly Betty" and for his extensive work in theater and film.
-
D.
Rob Riggle
Rob Riggle is an American actor, comedian, and former Marine officer known for his energetic, often over-the-top roles in film and television comedies.
-
E.
John Brydon
John Brydon was a British architect of the late 19th and early 20th centuries, known for designing prominent public buildings in London.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245823f4c8190ade442cdcc2c224a |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17d5a7c2881909a7aaacddd09f00c |
completed | April 29, 2026, 3:39 a.m. |
Created at: April 17, 2026, 3:31 p.m.