Triple
T21633342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cuban League |
E533889
|
entity |
| Predicate | team |
P3756
|
FINISHED |
| Object | Santa Clara |
—
|
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: Santa Clara | Statement: [Cuban League, team, Santa Clara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Santa Clara Context triple: [Cuban League, team, Santa Clara]
-
A.
Santa Clara
Santa Clara is a Silicon Valley city in California known for its high-tech industry presence, Levi’s Stadium, and Santa Clara University.
-
B.
Santa Clara
chosen
Santa Clara is a major city in central Cuba known as the capital of Villa Clara Province and a historic site of key battles in the Cuban Revolution.
-
C.
Santa Clara
Santa Clara is a settlement located within the Arraiján District in Panama.
-
D.
Santa Clara
Santa Clara is a small village located in Guadalupe County in the U.S. state of New Mexico.
-
E.
San Mateo
San Mateo is a landlocked municipality in the province of Rizal in the Philippines, known for its mix of suburban communities, hilly terrain, and proximity to Metro Manila.
- 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_69e0c465ae7481908577b7209fdb2a77 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef52185dbc819096ad2fc5b7d953f8 |
completed | April 27, 2026, 12:10 p.m. |
Created at: April 16, 2026, 6:35 p.m.