Triple
T22837854
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Díaz |
E565994
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Diaz |
—
|
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: Diaz | Statement: [Díaz, hasVariant, Diaz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diaz Context triple: [Díaz, hasVariant, Diaz]
-
A.
Diaz
chosen
Diaz is a common Spanish-language surname borne by numerous notable figures across politics, arts, sports, and history.
-
B.
Che Díaz
Che Díaz is a nonbinary, queer stand-up comedian and podcast host who becomes a central love interest and catalyst for change in the Sex and the City sequel series And Just Like That....
-
C.
Marco Diaz
Marco Diaz is a cautious yet brave teenage boy and skilled karate fighter who serves as one of the main protagonists alongside Star Butterfly in the animated series "Star vs. the Forces of Evil."
-
D.
Díaz
Díaz is a common Spanish surname borne by numerous notable figures in politics, arts, and sports across the Spanish-speaking world.
-
E.
Garza
Garza is a Spanish-language surname of Basque origin that is common in Mexico and among people of Hispanic heritage.
- 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_69e245869e188190a196584f36e682da |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17e303cec81909c5c118dc8c93354 |
completed | April 29, 2026, 3:42 a.m. |
Created at: April 17, 2026, 3:35 p.m.