Triple
T19898786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cagayan Province |
E478223
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object | Solana |
—
|
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: Solana | Statement: [Cagayan Province, hasMunicipality, Solana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Solana Context triple: [Cagayan Province, hasMunicipality, Solana]
-
A.
Solana
chosen
Solana is a municipality in the province of Cagayan in the Philippines, known for its agricultural economy and proximity to the Cagayan River.
-
B.
Solana
Solana is a high-performance, proof-of-stake blockchain platform designed for fast, low-cost decentralized applications and crypto transactions.
-
C.
Solana
Solana is a Spanish surname most notably associated with Javier Solana, a prominent diplomat and former NATO Secretary General.
-
D.
Nacha
Nacha is a wise and nurturing cook and caretaker in Laura Esquivel’s novel "Like Water for Chocolate," serving as a maternal figure to the protagonist Tita on the De la Garza family ranch.
-
E.
Potos
Potos is a coastal village and popular tourist resort on the Greek island of Thasos in the northern Aegean Sea.
- 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_69d8e520682081909892916424699bd5 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6593fbb348190afa7acf45af406ed |
completed | April 20, 2026, 4:50 p.m. |
Created at: April 10, 2026, 1:52 p.m.