Triple
T8485967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Descent |
E200832
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object |
Saskia Mulder
Saskia Mulder is a Dutch actress best known for her role in the British horror film "The Descent."
|
E736134
|
NE FINISHED |
How this triple was built (4 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: Saskia Mulder | Statement: [The Descent, stars, Saskia Mulder]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saskia Mulder Context triple: [The Descent, stars, Saskia Mulder]
-
A.
Saskia de Jonge
Saskia de Jonge is a Dutch former competitive swimmer who specialized in freestyle events and represented the Netherlands in international competitions, including the Olympic Games.
-
B.
Saskia Boddeke
Saskia Boddeke is a Dutch multimedia artist and director known for her immersive installations, opera stagings, and frequent collaborations with filmmaker Peter Greenaway.
-
C.
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.
-
D.
Maayke Velders
Maayke Velders is known primarily as the spouse of Dutch naval hero Michiel de Ruyter.
-
E.
Iris Steensma
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.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Saskia Mulder Triple: [The Descent, stars, Saskia Mulder]
Generated description
Saskia Mulder is a Dutch actress best known for her role in the British horror film "The Descent."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Saskia Mulder Target entity description: Saskia Mulder is a Dutch actress best known for her role in the British horror film "The Descent."
-
A.
Saskia de Jonge
Saskia de Jonge is a Dutch former competitive swimmer who specialized in freestyle events and represented the Netherlands in international competitions, including the Olympic Games.
-
B.
Saskia Boddeke
Saskia Boddeke is a Dutch multimedia artist and director known for her immersive installations, opera stagings, and frequent collaborations with filmmaker Peter Greenaway.
-
C.
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.
-
D.
Maayke Velders
Maayke Velders is known primarily as the spouse of Dutch naval hero Michiel de Ruyter.
-
E.
Iris Steensma
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.
- F. None of above. chosen
Provenance (5 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_69ca831d7b148190a6e32c1de43ab13b |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe53b359c81908174addcd6e12785 |
completed | March 31, 2026, 3:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce3a45e30c8190838ac499bbc66fbd |
completed | April 2, 2026, 9:43 a.m. |
| NEDg | Description generation | batch_69ce3c22f3c0819084803630d438c55e |
completed | April 2, 2026, 9:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce3ca57ec081909a14d962eee2c9a5 |
completed | April 2, 2026, 9:53 a.m. |
Created at: March 30, 2026, 6:12 p.m.