Triple
T23068410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Río Limay |
E575119
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | Senillosa |
—
|
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: Senillosa | Statement: [Río Limay, nearCity, Senillosa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Senillosa Context triple: [Río Limay, nearCity, Senillosa]
-
A.
Senillosa
chosen
Senillosa is a small town in Argentina’s Neuquén Province, known for its agricultural activities and proximity to the city of Neuquén.
-
B.
Osuna
Osuna is a historic town in the province of Seville, Spain, known for its rich archaeological heritage, including notable ancient reliefs and other Roman-era remains.
-
C.
Cabrils
Cabrils is a small municipality in the Maresme comarca of Catalonia, Spain, known for its residential character and proximity to the Mediterranean coast.
-
D.
Sariñena
Sariñena is a town in the province of Huesca, Aragon, Spain, known for its location in the semi-arid Los Monegros region and its surrounding agricultural landscape.
-
E.
Benacazón
Benacazón is a town in the province of Seville, Spain, known for serving as an endpoint on the Seville commuter rail network.
- 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_69e245bd6e4c8190bb8942245b68cad5 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f189a5aa4081909b3f0dc92877323d |
completed | April 29, 2026, 4:31 a.m. |
Created at: April 17, 2026, 3:55 p.m.