Triple
T19743646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Medellín River |
E474194
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Envigado |
—
|
NE NERFINISHED |
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: Envigado | Statement: [Medellín River, flowsThrough, Envigado]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Envigado Context triple: [Medellín River, flowsThrough, Envigado]
-
A.
Envigado
chosen
Envigado is a city in northwestern Colombia that forms part of the Medellín metropolitan area and is known for its residential character and quality of life.
-
B.
Vila-seca
Vila-seca is a coastal municipality in Catalonia, Spain, known for its tourism, proximity to Tarragona, and educational facilities including a campus of Rovira i Virgili University.
-
C.
Vinhedo
Vinhedo is a municipality in southeastern Brazil known for its high quality of life, proximity to Campinas, and attractions such as the Hopi Hari theme park and annual grape festival.
-
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.
Afogados
Afogados is a populous neighborhood in the Brazilian city of Recife, known for its busy commercial areas and dense urban character.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51940a0819087bd2996f98da668 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65162f0f88190b652fff01ee23090 |
completed | April 20, 2026, 4:16 p.m. |
Created at: April 10, 2026, 1:47 p.m.