Triple
T17466676
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Binangonan |
E425293
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Cardona |
—
|
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: Cardona | Statement: [Binangonan, borderedBy, Cardona]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cardona Context triple: [Binangonan, borderedBy, Cardona]
-
A.
Cardona
chosen
Cardona is a lakeside municipality in the Philippine province of Rizal known for its fishing industry and views of Laguna de Bay.
-
B.
Cardona
Cardona is a historic municipality in Catalonia, Spain, renowned for its medieval castle and ancient salt mountain.
-
C.
Martorell
Martorell is a town in Catalonia, Spain, known as an important industrial hub within the Barcelona metropolitan area.
-
D.
Bernalda
Bernalda is a historic town in southern Italy’s Basilicata region, known for its medieval center, proximity to the Ionian coast, and as the ancestral home of filmmaker Francis Ford Coppola.
-
E.
Manresa
Manresa is a historic city in Catalonia, Spain, known for its medieval architecture and significance as a religious and commercial center in the region.
- 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_69d889dbc2e88190b18ea6115e819258 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e451a7e2a481908c32defa3401f848 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 5:47 a.m.