Triple
T1180699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taxi Driver |
E25129
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Iris Steensma |
E40008
|
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: Iris Steensma | Statement: [Taxi Driver, featuresCharacter, Iris Steensma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Iris Steensma Context triple: [Taxi Driver, featuresCharacter, Iris Steensma]
-
A.
Iris Steensma
chosen
Iris Steensma is the troubled teenage prostitute character from Martin Scorsese’s 1976 film "Taxi Driver," whose role became iconic through Jodie Foster’s acclaimed performance.
-
B.
Maayke Velders
Maayke Velders is known primarily as the spouse of Dutch naval hero Michiel de Ruyter.
-
C.
Sjoukje Ozinga
Sjoukje Ozinga was the mother of Saskia van Uylenburgh, the Dutch woman best known as the wife and muse of painter Rembrandt van Rijn.
-
D.
Anna van Egmond
Anna van Egmond was a 16th-century Dutch noblewoman and heiress who became the first wife of William the Silent, Prince of Orange.
-
E.
Simone Buitendijk
Simone Buitendijk is a Dutch academic leader and scholar in higher education policy who has served as vice-chancellor of the University of Leeds.
- 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_69a494267b4c819088c97a59182bf56a |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd32c5f48190b4e2d39fa052cbb7 |
completed | March 1, 2026, 10:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac8311ba6481908aaca4c1e9d8b78f |
completed | March 7, 2026, 7:57 p.m. |
Created at: March 1, 2026, 7:45 p.m.