Triple
T6614295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nani |
E149309
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Nani |
E149309
|
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: Nani | Statement: [Nani, nickname, Nani]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nani Context triple: [Nani, nickname, Nani]
-
A.
Nani
chosen
Nani is a Portuguese professional footballer best known as a dynamic winger who starred for Manchester United and the Portugal national team, winning multiple domestic titles and the UEFA Euro 2016.
-
B.
Nani and Nana
"Nani and Nana" is a well-known chutney music song recognized for its catchy rhythm and popularity within Indo-Caribbean musical culture.
-
C.
Nandana
Nandana is a person known primarily as the sibling of Nanda, a figure mentioned in historical and/or cultural contexts.
-
D.
Raisa
Raisa Gorbacheva was the influential and highly visible wife of Soviet leader Mikhail Gorbachev, known for her intellectual background, public role, and charitable work.
-
E.
Nenê
Nenê is a Brazilian professional basketball player and longtime NBA center known for his physical interior play and key contributions to both the Denver Nuggets and Washington Wizards.
- 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_69c687ebc680819094caf71faba2efe2 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6af569ecc8190a9526decc745f0a0 |
completed | March 27, 2026, 4:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6eee61df88190b3772e4756670b8f |
completed | March 27, 2026, 8:56 p.m. |
Created at: March 27, 2026, 1:57 p.m.