Triple
T2892322
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rooney Mara |
E63855
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Her |
E50437
|
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: Her | Statement: [Rooney Mara, notableWork, Her]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Her Context triple: [Rooney Mara, notableWork, Her]
-
A.
Her
chosen
Her is a 2013 science-fiction romantic drama film directed by Spike Jonze that explores a man's emotional relationship with an advanced artificial intelligence operating system.
-
B.
HER
HER is the commonly used abbreviation for the Harvard Educational Review, a scholarly journal focused on education research and policy.
-
C.
She
"She" is a track by the American punk rock band Green Day from their breakthrough 1994 album *Dookie*.
-
D.
Fem
"Fem" is a 2020 Afrobeats hit single by Nigerian singer Davido, widely recognized as a bold, politically charged anthem that became prominent during the #EndSARS protests.
-
E.
He
He is a common Chinese surname borne by numerous notable figures across politics, military, arts, and academia.
- 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_69ab4c45822c8190830c5f2bb97bcfd0 |
completed | March 6, 2026, 9:51 p.m. |
| NER | Named-entity recognition | batch_69abe060f49c8190bc804614a141c738 |
completed | March 7, 2026, 8:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b0317e15248190bade0f0fd930581a |
completed | March 10, 2026, 2:58 p.m. |
Created at: March 6, 2026, 10:07 p.m.