Triple
T12358944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Itapira |
E294682
|
entity |
| Predicate | hasNeighboringMunicipality |
P224
|
FINISHED |
| Object |
Lindóia
Lindóia is a small spa town and municipality in the state of São Paulo, Brazil, known for its mineral water tourism.
|
E1070009
|
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: Lindóia | Statement: [Itapira, hasNeighboringMunicipality, Lindóia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lindóia Context triple: [Itapira, hasNeighboringMunicipality, Lindóia]
-
A.
Taubaté
Taubaté is a historic industrial and educational city in southeastern Brazil, located in the Paraíba Valley between São Paulo and Rio de Janeiro.
-
B.
Araraquara
Araraquara is a mid-sized city in southeastern Brazil known for its agricultural economy, especially sugarcane production, and its role as a regional commercial and educational center.
-
C.
Guarujá
Guarujá is a coastal resort city in southeastern Brazil known for its popular beaches and tourism.
-
D.
Santo Amaro
Santo Amaro is a central neighborhood in Recife, Brazil, known for its mix of residential areas, commerce, and important urban infrastructure.
-
E.
Valinhos
Valinhos is a municipality in southeastern Brazil known for its agricultural production, especially grapes and figs, and its proximity to the city of Campinas.
- 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: Lindóia Triple: [Itapira, hasNeighboringMunicipality, Lindóia]
Generated description
Lindóia is a small spa town and municipality in the state of São Paulo, Brazil, known for its mineral water tourism.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lindóia Target entity description: Lindóia is a small spa town and municipality in the state of São Paulo, Brazil, known for its mineral water tourism.
-
A.
Taubaté
Taubaté is a historic industrial and educational city in southeastern Brazil, located in the Paraíba Valley between São Paulo and Rio de Janeiro.
-
B.
Araraquara
Araraquara is a mid-sized city in southeastern Brazil known for its agricultural economy, especially sugarcane production, and its role as a regional commercial and educational center.
-
C.
Guarujá
Guarujá is a coastal resort city in southeastern Brazil known for its popular beaches and tourism.
-
D.
Santo Amaro
Santo Amaro is a central neighborhood in Recife, Brazil, known for its mix of residential areas, commerce, and important urban infrastructure.
-
E.
Valinhos
Valinhos is a municipality in southeastern Brazil known for its agricultural production, especially grapes and figs, and its proximity to the city of Campinas.
- 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_69d6ab6d8a4081908636601e69ddf262 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f90201481909359416b8b9f7871 |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7ce593bbc8190827ca217f43140b9 |
completed | May 3, 2026, 10:38 p.m. |
| NEDg | Description generation | batch_69f9fd56da288190b2bd33bc496c3fb9 |
completed | May 5, 2026, 2:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fb039fdb1c8190ad5286d1cfe80a29 |
completed | May 6, 2026, 9:02 a.m. |
Created at: April 8, 2026, 9:54 p.m.