Triple
T7517259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Geno Auriemma |
E177675
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Geno |
E177675
|
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: Geno | Statement: [Geno Auriemma, nickname, Geno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Geno Context triple: [Geno Auriemma, nickname, Geno]
-
A.
Geno
chosen
Geno is the widely used nickname of Hall of Fame University of Connecticut women's basketball coach Geno Auriemma.
-
B.
Geneta
Geneta is a residential district and suburb within Södertälje Municipality in Sweden.
-
C.
Genn
Genn is a surname most notably associated with British actor and barrister Leo Genn.
-
D.
Segeneiti
Segeneiti is a town in southern Eritrea known for its agricultural surroundings and role as a local commercial center.
-
E.
Genna
Genna is the Ethiopian Orthodox celebration of Christmas, observed on January 7 with distinctive religious services, traditional games, and communal festivities.
- 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_69c69f2891148190a484f3b8222c6f1b |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5f6ccc8819080ffd123fdd59a50 |
completed | March 27, 2026, 9:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c84621ba688190b85ee787b856b138 |
completed | March 28, 2026, 9:20 p.m. |
Created at: March 27, 2026, 3:46 p.m.