Triple
T5984681
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaz |
E133197
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Kaz |
E133197
|
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: [Kaz, name, Kaz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kaz Context triple: [Kaz, name, Kaz]
-
A.
Kaz
chosen
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 the three-letter ISO 3166-1 alpha-3 country code assigned to Kazakhstan for international standardization and identification.
-
C.
Kas
Kas is the historical name of Shahrisabz, an ancient city in southern Uzbekistan renowned as the birthplace of Timur (Tamerlane) and for its significant Timurid-era architectural monuments.
-
D.
Katsuya
Katsuya is a Japanese given name commonly used for males.
-
E.
Kai
Kai is the eldest granddaughter of former U.S. President Donald Trump and the daughter of Donald Trump Jr.
- 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_69c0087010d081908bb8142342d63330 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04a6dcaf08190bac27c7042e65e07 |
completed | March 22, 2026, 8 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1084e9ca481909f585b3d19991a60 |
completed | March 23, 2026, 9:30 a.m. |
Created at: March 22, 2026, 4:04 p.m.