Triple
T15329341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Entre Ríos |
E366491
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Gualeguay |
E640308
|
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: Gualeguay | Statement: [Entre Ríos, hasCity, Gualeguay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gualeguay Context triple: [Entre Ríos, hasCity, Gualeguay]
-
A.
Gualeguay
chosen
Gualeguay is a city in eastern Argentina known for its agricultural economy and location along the Gualeguay River in Entre Ríos Province.
-
B.
Villaguay
Villaguay is a city in central Argentina known as an important agricultural and commercial hub within Entre Ríos Province.
-
C.
Río Rocha
Río Rocha is a river in central Bolivia that flows through the city of Cochabamba and plays a key role in the region’s drainage and environmental conditions.
-
D.
Gualeguaychú River
The Gualeguaychú River is a watercourse in Entre Ríos Province, Argentina, known for flowing past the city of Gualeguaychú and into the Uruguay River.
-
E.
Ríos Rosas
Ríos Rosas is a Madrid Metro station located in the Chamberí district, serving as part of the city's historic Line 1.
- 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_69d85a121520819093dcce999fdefe1a |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e0161ac8190aa1d52c063c02ad0 |
completed | April 16, 2026, 1:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff01ecb904819082454622dcd77556 |
completed | May 9, 2026, 9:44 a.m. |
Created at: April 10, 2026, 3:16 a.m.