Triple
T2720489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Congonhas–São Paulo Airport |
E60068
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Congonhas
Congonhas is a district in the city of São Paulo, Brazil, best known for giving its name to one of the country’s busiest domestic airports.
|
E302062
|
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: Congonhas | Statement: [Congonhas–São Paulo Airport, namedAfter, Congonhas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Congonhas Context triple: [Congonhas–São Paulo Airport, namedAfter, Congonhas]
-
A.
Icó
Icó is a historic municipality in northeastern Brazil known for its colonial architecture and cultural heritage within the state of Ceará.
-
B.
Caieiras
Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
-
C.
Caicó
Caicó is a municipality in the interior of Rio Grande do Norte, Brazil, known for its strong cultural traditions, especially its famous religious festivals and regional cuisine.
-
D.
Ourinhos
Ourinhos is a municipality in the southwestern part of the state of São Paulo, Brazil, known as a regional commercial and agricultural center.
-
E.
Mauá
Mauá is an industrial and residential city located in the metropolitan region of São Paulo, Brazil.
- 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: Congonhas Triple: [Congonhas–São Paulo Airport, namedAfter, Congonhas]
Generated description
Congonhas is a district in the city of São Paulo, Brazil, best known for giving its name to one of the country’s busiest domestic airports.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Congonhas Target entity description: Congonhas is a district in the city of São Paulo, Brazil, best known for giving its name to one of the country’s busiest domestic airports.
-
A.
Icó
Icó is a historic municipality in northeastern Brazil known for its colonial architecture and cultural heritage within the state of Ceará.
-
B.
Caieiras
Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
-
C.
Caicó
Caicó is a municipality in the interior of Rio Grande do Norte, Brazil, known for its strong cultural traditions, especially its famous religious festivals and regional cuisine.
-
D.
Ourinhos
Ourinhos is a municipality in the southwestern part of the state of São Paulo, Brazil, known as a regional commercial and agricultural center.
-
E.
Mauá
Mauá is an industrial and residential city located in the metropolitan region of São Paulo, Brazil.
- 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_69ab4b746d248190958e052045c09255 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdab06d388190acf690787fe58ab5 |
completed | March 7, 2026, 7:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afce8317fc8190ab3736b950b92495 |
completed | March 10, 2026, 7:55 a.m. |
| NEDg | Description generation | batch_69afcef4f8f881908deab641abe6586e |
completed | March 10, 2026, 7:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afcf60d3c88190a2e2bf49cd1ec766 |
completed | March 10, 2026, 7:59 a.m. |
Created at: March 6, 2026, 9:55 p.m.