Triple
T6142643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sander Loones |
E136997
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Sander Loones |
E136997
|
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: Sander Loones | Statement: [Sander Loones, name, Sander Loones]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sander Loones Context triple: [Sander Loones, name, Sander Loones]
-
A.
Sander Loones
chosen
Sander Loones is a Belgian politician and member of the New Flemish Alliance (N-VA) who has served in both national and European political roles.
-
B.
Sander Jacobs
Sander Jacobs is a film and television producer known for his work on the acclaimed musical film adaptation of "Hamilton."
-
C.
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.
-
D.
Adrian Sweere
Adrian Sweere was an early 20th-century Jesuit priest and educator who played a key role in establishing what would become Seattle University.
-
E.
Marc de Jonge
Marc de Jonge was a French actor best known internationally for playing the Soviet Colonel Zaysen in the action film "Rambo III."
- 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_69c008a2c6308190a56519b22d55d083 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05cb387ac8190a60579b59a741425 |
completed | March 22, 2026, 9:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c135f88db881908b8a5d9c35bf0fbb |
completed | March 23, 2026, 12:45 p.m. |
Created at: March 22, 2026, 4:16 p.m.