Triple
T8169548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Minas Gerais |
E190780
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Betim
Betim is an industrial city in southeastern Brazil known for its major automotive and petrochemical complexes.
|
E722582
|
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: Betim | Statement: [Minas Gerais, containsCity, Betim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Betim Context triple: [Minas Gerais, containsCity, Betim]
-
A.
Uberaba
Uberaba is a mid-sized Brazilian city in the western part of Minas Gerais state, known for its strong agribusiness sector and cattle breeding traditions.
-
B.
Itanhaém
Itanhaém is a coastal municipality in southeastern Brazil known for its beaches, historic colonial center, and tourism along the São Paulo state shoreline.
-
C.
Itapetininga
Itapetininga is a municipality in southeastern Brazil known for its agricultural activities and regional commercial importance within the state of São Paulo.
-
D.
Morrinhos
Morrinhos is a municipality in the Brazilian state of Goiás, known for its agricultural economy and regional thermal springs.
-
E.
Barueri
Barueri is a rapidly developing municipality in the São Paulo metropolitan area of Brazil, known for its strong commercial sector and high standard of living.
- 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: Betim Triple: [Minas Gerais, containsCity, Betim]
Generated description
Betim is an industrial city in southeastern Brazil known for its major automotive and petrochemical complexes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Betim Target entity description: Betim is an industrial city in southeastern Brazil known for its major automotive and petrochemical complexes.
-
A.
Uberaba
Uberaba is a mid-sized Brazilian city in the western part of Minas Gerais state, known for its strong agribusiness sector and cattle breeding traditions.
-
B.
Itanhaém
Itanhaém is a coastal municipality in southeastern Brazil known for its beaches, historic colonial center, and tourism along the São Paulo state shoreline.
-
C.
Itapetininga
Itapetininga is a municipality in southeastern Brazil known for its agricultural activities and regional commercial importance within the state of São Paulo.
-
D.
Morrinhos
Morrinhos is a municipality in the Brazilian state of Goiás, known for its agricultural economy and regional thermal springs.
-
E.
Barueri
Barueri is a rapidly developing municipality in the São Paulo metropolitan area of Brazil, known for its strong commercial sector and high standard of living.
- 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_69ca82c1c0a08190bf8692b4d91a03ca |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb4803de688190960438aa059d163b |
completed | March 31, 2026, 4:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd67b82424819082fdbfe9bf2a9839 |
completed | April 1, 2026, 6:45 p.m. |
| NEDg | Description generation | batch_69cd6c1f75748190b119acd0d92f2ef9 |
completed | April 1, 2026, 7:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cd7da4f3a0819080eed3d03c293789 |
completed | April 1, 2026, 8:18 p.m. |
Created at: March 30, 2026, 5:39 p.m.