Triple
T14956572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Derek Zoolander |
E372944
|
entity |
| Predicate | hasRival |
P1375
|
FINISHED |
| Object | Hansel McDonald |
E365681
|
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: Hansel McDonald | Statement: [Derek Zoolander, hasRival, Hansel McDonald]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hansel McDonald Context triple: [Derek Zoolander, hasRival, Hansel McDonald]
-
A.
Hansel McDonald
chosen
Hansel McDonald is the laid-back, free-spirited male supermodel character from the "Zoolander" comedy films.
-
B.
Mac Otten
Mac Otten was an American professional basketball player who competed in the early years of the National Basketball Association.
-
C.
Christian Anderson
Christian Anderson is a stage actor known for originating the role of Barry in the Broadway production of the musical "High Fidelity."
-
D.
Mickey Vernon
Mickey Vernon was an American Major League Baseball first baseman and two-time batting champion best known for his long and distinguished career, primarily with the Washington Senators.
-
E.
Sam McCandlish
Sam McCandlish is a machine learning researcher known for his work on large-scale language models and contributions to influential AI research at OpenAI.
- 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6cc73848190ac181782b20dc838 |
completed | April 15, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe7e9e74fc8190bdd10a25c39829f3 |
completed | May 9, 2026, 12:23 a.m. |
Created at: April 10, 2026, 2:40 a.m.