Triple
T22297857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tara Elders |
E551168
|
entity |
| Predicate | marriageStart |
P198
|
FINISHED |
| Object | Michiel Huisman, 2008 |
—
|
NE NERFINISHED |
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: Michiel Huisman, 2008 | Statement: [Tara Elders, marriageStart, Michiel Huisman, 2008]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michiel Huisman, 2008 Context triple: [Tara Elders, marriageStart, Michiel Huisman, 2008]
-
A.
Michiel Huisman
chosen
Michiel Huisman is a Dutch actor and musician known for roles in international film and television, including series like Game of Thrones and The Haunting of Hill House.
-
B.
Theo Huisman
Theo Huisman is a person notable enough to be recognized as a prominent bearer of the Dutch surname Huisman.
-
C.
Willem Huisman
Willem Huisman is a notable individual who carries the Dutch surname Huisman, recognized as a distinguished bearer of that name.
-
D.
Sjoerd Huisman
Sjoerd Huisman was a Dutch marathon speed skater known for his successes in long-distance skating competitions before his untimely death in 2013.
-
E.
Jeroen Huisman
Jeroen Huisman is a scholar known for his research on higher education policy, governance, and organization.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e45fb848190a1b2ae21296e3a5f |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1572200c88190b9413286136fef15 |
completed | April 29, 2026, 12:56 a.m. |
Created at: April 16, 2026, 8:41 p.m.