Triple
T21174155
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Entre Ríos Province |
E521763
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Gualeguaychú |
—
|
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: Gualeguaychú | Statement: [Entre Ríos Province, hasMajorCity, Gualeguaychú]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gualeguaychú Context triple: [Entre Ríos Province, hasMajorCity, Gualeguaychú]
-
A.
Gualeguaychú
chosen
Gualeguaychú is a city in eastern Argentina known for its vibrant Carnival celebrations and riverside tourism.
-
B.
Tandil
Tandil is a mid-sized city in central Argentina known for its scenic hilly landscapes, stone formations, and tourism-focused outdoor activities.
-
C.
Río Cuarto
Río Cuarto is a major city in central Argentina known as an important commercial, agricultural, and educational hub within Córdoba Province.
-
D.
Catanduva
Catanduva is a municipality in the northwestern region of the state of São Paulo, Brazil, known for its agricultural production and regional commercial importance.
-
E.
San Justo
San Justo is a city in the Buenos Aires Province of Argentina, forming part of the Greater Buenos Aires metropolitan area.
- 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_69e0b50e30748190b186824a206d39b9 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e72714d3f48190871c5e35c3887d7f |
completed | April 21, 2026, 7:28 a.m. |
Created at: April 16, 2026, 3 p.m.