Triple
T10159449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kurt Vonnegut |
E233850
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Kurt |
E102649
|
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: Kurt | Statement: [Kurt Vonnegut, givenName, Kurt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kurt Context triple: [Kurt Vonnegut, givenName, Kurt]
-
A.
Kurt
chosen
Kurt is a given name most famously associated with the logician and mathematician Kurt Gödel.
-
B.
Kurt
Kurt is a close associate of Scott Lang in the Marvel universe, known as one of his quirky ex-con partners in crime and heists.
-
C.
Kurt Christian
Kurt Christian is a British actor best known for his roles in 1970s fantasy and adventure films, particularly in Ray Harryhausen–associated productions.
-
D.
Kurt Weiss
Kurt Weiss is an individual notable enough to be recognized as a namesake of the surname Weiss, though specific widely known biographical details are not clearly established.
-
E.
Kurt Back
Kurt Back was a social psychologist known for his collaborative research in social behavior and group dynamics, including work with Leon Festinger.
- 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_69ca848e80748190b91d1e04d35512c7 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cdec56d944819081bc6ea36c905ba2 |
completed | April 2, 2026, 4:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d300b7b3108190a8e7581193c322be |
completed | April 6, 2026, 12:39 a.m. |
Created at: March 30, 2026, 9:09 p.m.