Triple
T15723002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Runway 09L/27R |
E381147
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Guarulhos |
E293044
|
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: Guarulhos | Statement: [Runway 09L/27R, locatedIn, Guarulhos]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guarulhos Context triple: [Runway 09L/27R, locatedIn, Guarulhos]
-
A.
Guarulhos
chosen
Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
-
B.
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.
-
C.
Campinas
Campinas is a major city in the state of São Paulo, Brazil, known as an important industrial, technological, and transportation hub in the country.
-
D.
Taubaté
Taubaté is a historic industrial and educational city in southeastern Brazil, located in the Paraíba Valley between São Paulo and Rio de Janeiro.
-
E.
Ribeirão Preto
Ribeirão Preto is a major city in the state of São Paulo, Brazil, known as an important economic and cultural center with a strong agribusiness and services sector.
- 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04fb1fdd4819088f3e243263e5f73 |
completed | April 16, 2026, 2:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff997fe6f48190813bde2bfc11c253 |
completed | May 9, 2026, 8:30 p.m. |
Created at: April 10, 2026, 4:46 a.m.