Triple
T21397423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fellow Travelers |
E527820
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Allison Williams |
—
|
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: Allison Williams | Statement: [Fellow Travelers, starring, Allison Williams]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Allison Williams Context triple: [Fellow Travelers, starring, Allison Williams]
-
A.
Allison Williams
chosen
Allison Williams is an American actress and singer best known for her roles in the HBO series "Girls" and the horror film "Get Out."
-
B.
Kat Dennings
Kat Dennings is an American actress best known for her roles in the sitcom "2 Broke Girls" and films such as "Nick and Norah's Infinite Playlist" and the Marvel "Thor" series.
-
C.
Taissa Farmiga
Taissa Farmiga is an American actress best known for her recurring roles in the horror anthology series "American Horror Story" and films such as "The Nun."
-
D.
Rooney Mara
Rooney Mara is an American actress known for her acclaimed performances in films such as "The Girl with the Dragon Tattoo" and "Carol."
-
E.
Olivia Munn
Olivia Munn is an American actress and television personality known for roles in projects like "The Newsroom," "X-Men: Apocalypse," and various comedy and action films.
- 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_69e0b51ff3748190935c0a513c62a12b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b16a9c7c819083bd2d298106fdf1 |
completed | April 22, 2026, 11:30 a.m. |
Created at: April 16, 2026, 5:13 p.m.