Triple
T22698421
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Cuenca |
E561247
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Cuenca |
—
|
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: Cuenca | Statement: [University of Cuenca, city, Cuenca]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cuenca Context triple: [University of Cuenca, city, Cuenca]
-
A.
Cuenca
Cuenca is a historic Spanish city renowned for its medieval architecture and dramatic “hanging houses” perched above deep river gorges.
-
B.
Cuenca
chosen
Cuenca is a historic city in southern Ecuador known for its well-preserved colonial architecture and cultural significance.
-
C.
Cuenca
Cuenca is a landlocked municipality in the province of Batangas in the Philippines, known for Mount Macolod and its agricultural communities.
-
D.
Calarcá
Calarcá is a Colombian town and municipality in the coffee-growing Quindío Department, known for its cultural heritage and role in the Coffee Cultural Landscape.
-
E.
Tulcán
Tulcán is a city in northern Ecuador, capital of Carchi Province, known as a key Andean border crossing with Colombia and for its famous topiary cemetery.
- 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_69e2454e615481909c177440be559d2c |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f178a008448190b393335704128fe8 |
completed | April 29, 2026, 3:18 a.m. |
Created at: April 17, 2026, 3:14 p.m.