Triple
T15636723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christine (2016 film) |
E375963
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | Saunder Jurriaans |
E431761
|
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: Saunder Jurriaans | Statement: [Christine (2016 film), musicBy, Saunder Jurriaans]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saunder Jurriaans Context triple: [Christine (2016 film), musicBy, Saunder Jurriaans]
-
A.
Saunder Jurriaans
chosen
Saunder Jurriaans is an American composer and musician best known for his atmospheric film and television scores, often created in collaboration with Danny Bensi.
-
B.
Sander van Doorn
Sander van Doorn is a Dutch DJ and electronic music producer known for his influential work in trance and progressive house.
-
C.
Sjoerd Soeters
Sjoerd Soeters is a Dutch architect known for his postmodern, human-scaled urban designs and influential waterfront redevelopment projects in the Netherlands.
-
D.
Christian Huitema
Christian Huitema is a French computer scientist and Internet pioneer known for his influential work on networking protocols and IPv6 transition technologies.
-
E.
Sander Dieleman
Sander Dieleman is a machine learning researcher known for his influential work in deep learning for audio and music, including contributions to models such as WaveNet.
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04eba51f08190ac5d9de7fc89405a |
completed | April 16, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffcf0bc4e88190be83324776b26df9 |
completed | May 10, 2026, 12:19 a.m. |
Created at: April 10, 2026, 4:14 a.m.