Triple
T20521535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benjamin Davis Wilson |
E503819
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Don Benito |
—
|
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: Don Benito | Statement: [Benjamin Davis Wilson, nickname, Don Benito]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Don Benito Context triple: [Benjamin Davis Wilson, nickname, Don Benito]
-
A.
Don Benito
chosen
Don Benito is a town in the province of Badajoz, in Spain’s Extremadura region, known for its agricultural economy and close association with the nearby town of Villanueva de la Serena.
-
B.
Don Marcelino
Don Marcelino is a coastal municipality in the province of Davao Occidental in the Philippines, known for its agricultural and fishing-based local economy.
-
C.
Rico Santo
Rico Santo is an individual notable enough to be recognized as a prominent bearer of the surname Santo.
-
D.
Juan Benet
Juan Benet is a computer scientist and entrepreneur best known as the founder of Protocol Labs and the creator of the InterPlanetary File System (IPFS) and Filecoin.
-
E.
Rafael Banquells
Rafael Banquells was a Cuban-born Mexican actor and director known for his work in classic Mexican cinema and television.
- 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_69e0b4b2aa788190ae9eb37c1d73b1f1 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69f46488c819093687b4e07837793 |
completed | April 20, 2026, 9:48 p.m. |
Created at: April 16, 2026, 11:36 a.m.