Triple
T18380033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ron Leibman |
E446417
|
entity |
| Predicate | awardReceivedFor |
P107
|
FINISHED |
| Object | Kaz |
E1321472
|
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: Kaz | Statement: [Ron Leibman, awardReceivedFor, Kaz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kaz Context triple: [Ron Leibman, awardReceivedFor, Kaz]
-
A.
Kaz
Kaz is one of the futuristic, computer-generated Spheriks characters that served as an official mascot for the 2002 FIFA World Cup in South Korea and Japan.
-
B.
Kaz
Kaz is a person known for working closely with Nik as a teammate, likely in a collaborative or competitive setting such as sports, gaming, or a professional project.
-
C.
Kaz
Kaz is a central protagonist in the Disney XD series "Mighty Med," known as a comic book fan who becomes a sidekick and caretaker to real-life superheroes.
-
D.
Kaz
chosen
Kaz is an American sitcom television series created by and starring Ron Leibman as a reformed criminal turned lawyer.
-
E.
KAZ
KAZ is the three-letter ISO 3166-1 alpha-3 country code assigned to Kazakhstan for international standardization and identification.
- 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_69d8b9f370b88190b1e5081c2c238e7f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e5179aa328819097f5ed8193cfa401 |
completed | April 19, 2026, 5:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03e23a82348190acde1122b6eb75b0 |
completed | May 13, 2026, 2:30 a.m. |
Created at: April 10, 2026, 10:45 a.m.