Triple
T18163853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gilberto |
E434837
|
entity |
| Predicate | hasDiminutive |
P456
|
FINISHED |
| Object | Giba |
—
|
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: Giba | Statement: [Gilberto, hasDiminutive, Giba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Giba Context triple: [Gilberto, hasDiminutive, Giba]
-
A.
Giba
chosen
Giba is the nickname of Gilberto Amauri de Godoy Filho, a renowned Brazilian volleyball player considered one of the sport’s all-time greats.
-
B.
Gibo
Gibo is the nickname of Italian former professional road cyclist Gilberto Simoni, best known as a two-time Giro d'Italia winner and climbing specialist.
-
C.
Osanobua
Osanobua is the supreme creator god and central deity in the traditional religion and cosmology of the Edo people of Nigeria.
-
D.
Gishin
Gishin was an early Japanese Tendai Buddhist monk and scholar who helped establish and systematize the Tendai school following the teachings of his mentor Dengyō Daishi (Saichō).
-
E.
Gembu
Gembu is a town located on the Mambilla Plateau in Taraba State, eastern Nigeria, known for its cool climate and scenic highland landscapes.
- 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_69d8b90b7a188190b3fc7b8d4a6cd20a |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4dec55a088190868ee0b0a310fefb |
completed | April 19, 2026, 1:55 p.m. |
Created at: April 10, 2026, 10:30 a.m.