Triple
T15832696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michel Temer |
E383909
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Temer |
E383909
|
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: Temer | Statement: [Michel Temer, familyName, Temer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Temer Context triple: [Michel Temer, familyName, Temer]
-
A.
Michel Temer
chosen
Michel Temer is a Brazilian lawyer and politician who served as the 37th President of Brazil from 2016 to 2018 following the impeachment of Dilma Rousseff.
-
B.
Pedro Rousseff
Pedro Rousseff is the son of former Brazilian president Dilma Rousseff.
-
C.
Alexandre Gusmão
Alexandre Gusmão was a prominent 17th–18th century Portuguese-Brazilian diplomat and negotiator, best known for helping define Brazil’s borders through the Treaty of Madrid (1750).
-
D.
Lula
Lula is a feminine given name, often used in English-speaking countries and sometimes as a diminutive of names like Louise or Tallulah.
-
E.
Dilma Rousseff
Dilma Rousseff is a Brazilian economist and politician who served as the 36th president of Brazil and the country’s first female head of state.
- 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_69d86da34c888190976e06c4019d415a |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e11e653e388190a4696cdb22546715 |
completed | April 16, 2026, 5:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa135be84819084f7c20c2bc01b47 |
completed | May 9, 2026, 9:03 p.m. |
Created at: April 10, 2026, 4:49 a.m.