Triple
T16035124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coyotito |
E388951
|
entity |
| Predicate | hasFather |
P1908
|
FINISHED |
| Object | Kino |
E388950
|
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: Kino | Statement: [Coyotito, hasFather, Kino]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kino Context triple: [Coyotito, hasFather, Kino]
-
A.
Kino
chosen
Kino is the impoverished Mexican-Indian pearl diver and tragic protagonist of John Steinbeck’s novella "The Pearl."
-
B.
Kino Loy
Kino Loy is a character in the Star Wars series "Andor," known as a hardened yet principled inmate who becomes a key leader in the Narkina 5 prison uprising.
-
C.
Kazansky
Kazansky refers to Tom "Iceman" Kazansky, the elite U.S. Navy fighter pilot character from the "Top Gun" film series.
-
D.
Tartakovsky
Tartakovsky is a surname most prominently associated with Genndy Tartakovsky, the acclaimed animator and creator of series like Dexter’s Laboratory, Samurai Jack, and Primal.
-
E.
Nozdryov
Nozdryov is a boisterous, dishonest, and troublemaking landowner in Nikolai Gogol's novel "Dead Souls," known for his compulsive lying and love of gambling and chaos.
- 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_69d86dada3808190825d5f80d72fbe88 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1833b84608190887dafda5d081dc0 |
completed | April 17, 2026, 12:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffe474f89c819086db832b793c15ed |
completed | May 10, 2026, 1:50 a.m. |
Created at: April 10, 2026, 4:56 a.m.