Triple
T20680779
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cole Young |
E508284
|
entity |
| Predicate | notableOpponent |
P893
|
FINISHED |
| Object | Goro |
—
|
NE NERFINISHED |
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: Goro | Statement: [Cole Young, notableOpponent, Goro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Goro Context triple: [Cole Young, notableOpponent, Goro]
-
A.
Goro
chosen
Goro is a four-armed Shokan prince and powerful sub-boss character from the Mortal Kombat fighting game series.
-
B.
Goro
Goro is a town located in the Bale Zone of the Oromia Region in southeastern Ethiopia.
-
C.
Goro
Goro is a coastal fishing town and municipality in Italy’s Emilia-Romagna region, known for its clam and mussel production along the Po River delta.
-
D.
Goro
Goro is a character in Puccini’s opera "Madama Butterfly," a marriage broker who arranges the ill-fated union between Cio-Cio San and the American naval officer Pinkerton.
-
E.
Ryogo
Ryogo is a Japanese given name most notably borne by theoretical physicist Ryogo Kubo, known for his contributions to statistical mechanics and the fluctuation-dissipation theorem.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4c1164881909a3bf1e3ddb2bc32 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6bea655488190bab168d564d888af |
completed | April 21, 2026, 12:02 a.m. |
Created at: April 16, 2026, 11:45 a.m.