Triple
T6469571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tupian |
E142313
|
entity |
| Predicate | majorBranch |
P1185
|
FINISHED |
| Object |
Puruborá
Puruborá is an indigenous language of Brazil belonging to the Tupian family, traditionally spoken by the Puruborá people of the Amazon region.
|
E598486
|
NE FINISHED |
How this triple was built (4 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: Puruborá | Statement: [Tupian, majorBranch, Puruborá]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Puruborá Context triple: [Tupian, majorBranch, Puruborá]
-
A.
Ciluba
Ciluba is a Bantu language spoken primarily in the Democratic Republic of the Congo, especially in the Kasai region.
-
B.
Pirassununga
Pirassununga is a municipality in the state of São Paulo, Brazil, known for its agricultural activities and as a site of a major University of São Paulo campus.
-
C.
Borba
Borba is a town and municipality in Portugal’s Alentejo region, noted for its wine production and marble quarries.
-
D.
Coruripe
Coruripe is a coastal municipality in northeastern Brazil known for its beaches, fishing activities, and sugarcane agriculture.
-
E.
Itabaiana
Itabaiana is a prominent inland city in the Brazilian state of Sergipe, known for its vibrant commerce, agricultural production, and strategic location as a regional hub.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Puruborá Triple: [Tupian, majorBranch, Puruborá]
Generated description
Puruborá is an indigenous language of Brazil belonging to the Tupian family, traditionally spoken by the Puruborá people of the Amazon region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Puruborá Target entity description: Puruborá is an indigenous language of Brazil belonging to the Tupian family, traditionally spoken by the Puruborá people of the Amazon region.
-
A.
Ciluba
Ciluba is a Bantu language spoken primarily in the Democratic Republic of the Congo, especially in the Kasai region.
-
B.
Pirassununga
Pirassununga is a municipality in the state of São Paulo, Brazil, known for its agricultural activities and as a site of a major University of São Paulo campus.
-
C.
Borba
Borba is a town and municipality in Portugal’s Alentejo region, noted for its wine production and marble quarries.
-
D.
Coruripe
Coruripe is a coastal municipality in northeastern Brazil known for its beaches, fishing activities, and sugarcane agriculture.
-
E.
Itabaiana
Itabaiana is a prominent inland city in the Brazilian state of Sergipe, known for its vibrant commerce, agricultural production, and strategic location as a regional hub.
- F. None of above. chosen
Provenance (5 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_69c008d3bf4c8190bcf798c5ba9d6fb3 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06a16272c81909313455002cd884d |
completed | March 22, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c67403f7bc81908020e7f488121f8f |
completed | March 27, 2026, 12:11 p.m. |
| NEDg | Description generation | batch_69c6754b0a2c81908cec3d683f8117fc |
completed | March 27, 2026, 12:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6766d8eb48190bdcb6e494b04d90b |
completed | March 27, 2026, 12:22 p.m. |
Created at: March 22, 2026, 4:50 p.m.