Triple
T11893220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Assis Chateaubriand |
E282970
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Assis |
E796423
|
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: Assis | Statement: [Assis Chateaubriand, givenName, Assis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Assis Context triple: [Assis Chateaubriand, givenName, Assis]
-
A.
Assis
Assis is a municipality in the western part of the state of São Paulo, Brazil, known as a regional commercial and educational center.
-
B.
Azevêdo
Azevêdo is a Portuguese-language surname commonly found in Brazil and Portugal, associated with several notable public figures.
-
C.
Tadeu
chosen
Tadeu is a given name, primarily used in Portuguese-speaking countries, that is a variant of the name Tadeusz.
-
D.
Assin Foso
Assin Foso is a prominent town in Ghana that serves as a commercial and administrative hub within the country’s Central Region.
-
E.
Andrade
Andrade is a common Portuguese and Spanish surname borne by numerous notable figures across fields such as sports, politics, and the arts.
- 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_69d6ab2a90b08190a4e818821cc93e6d |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8dd1172988190a2c13d37220f2f93 |
completed | April 10, 2026, 11:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f4180569ac81909137d56374e800c0 |
completed | May 1, 2026, 3:03 a.m. |
Created at: April 8, 2026, 9:44 p.m.