Triple
T20169276
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paulo Costa |
E491912
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Paulo Henrique Costa |
—
|
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: Paulo Henrique Costa | Statement: [Paulo Costa, fullName, Paulo Henrique Costa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paulo Henrique Costa Context triple: [Paulo Costa, fullName, Paulo Henrique Costa]
-
A.
Paulo Costa
chosen
Paulo Costa is a Brazilian mixed martial artist known for competing in the UFC’s middleweight division and for his aggressive striking style.
-
B.
Alexandre Soares dos Santos
Alexandre Soares dos Santos was a prominent Portuguese businessman best known for leading and expanding the Jerónimo Martins retail group into an international supermarket and distribution giant.
-
C.
Thiago Soares
Thiago Soares is a renowned Brazilian ballet dancer best known as a former principal dancer of The Royal Ballet in London.
-
D.
Daniel Etcheverry
Daniel Etcheverry is an individual notable enough to be recognized as a prominent bearer of the Etcheverry surname.
-
E.
Thiago Teixeira
Thiago Teixeira is a technology entrepreneur best known as a co-founder of the data app development platform Streamlit.
- 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_69da6266c6888190bc1a3ecf24814d34 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e66846f4ec81908b0dc6a6e0ec27dd |
completed | April 20, 2026, 5:54 p.m. |
Created at: April 11, 2026, 11:35 p.m.