Triple
T12504802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nueva Vizcaya |
E298920
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Solano
Solano is a first-class municipality in the province of Nueva Vizcaya in the Philippines, known as one of its major commercial and trading centers.
|
E993566
|
NE FINISHED |
How this triple was built (4 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: Solano | Statement: [Nueva Vizcaya, hasCity, Solano]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Solano Context triple: [Nueva Vizcaya, hasCity, Solano]
-
A.
Solano
Solano is a rural municipality in southern Colombia known for its vast Amazonian rainforest areas and low population density within the Caquetá Department.
-
B.
Solano County
Solano County is a county in the San Francisco Bay Area–Sacramento Valley region known for its mix of suburban communities, agriculture, and key transportation corridors.
-
C.
Alameda
Alameda is the main central avenue of Santiago, Chile, serving as a key thoroughfare and symbolic axis of the city.
-
D.
Alameda
Alameda is a major Lisbon metro and transport hub that serves as a key interchange point within the city's public transit network.
-
E.
Santa Clara
Santa Clara is a Silicon Valley city in California known for its high-tech industry presence, Levi’s Stadium, and Santa Clara University.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Solano Triple: [Nueva Vizcaya, hasCity, Solano]
Generated description
Solano is a first-class municipality in the province of Nueva Vizcaya in the Philippines, known as one of its major commercial and trading centers.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Solano Target entity description: Solano is a first-class municipality in the province of Nueva Vizcaya in the Philippines, known as one of its major commercial and trading centers.
-
A.
Solano
Solano is a rural municipality in southern Colombia known for its vast Amazonian rainforest areas and low population density within the Caquetá Department.
-
B.
Solano County
Solano County is a county in the San Francisco Bay Area–Sacramento Valley region known for its mix of suburban communities, agriculture, and key transportation corridors.
-
C.
Alameda
Alameda is the main central avenue of Santiago, Chile, serving as a key thoroughfare and symbolic axis of the city.
-
D.
Alameda
Alameda is a major Lisbon metro and transport hub that serves as a key interchange point within the city's public transit network.
-
E.
Santa Clara
Santa Clara is a Silicon Valley city in California known for its high-tech industry presence, Levi’s Stadium, and Santa Clara University.
- F. None of above. chosen
Provenance (5 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_69d6ada4cd388190ae3bbf83ff87057a |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94dfddf38819099263b8b1e804736 |
completed | April 10, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65eac74608190a6f1941ed5a05212 |
completed | May 2, 2026, 8:29 p.m. |
| NEDg | Description generation | batch_69f65fadc97081908376913e390cfc3d |
completed | May 2, 2026, 8:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f660c3d914819097b57784889ca389 |
completed | May 2, 2026, 8:38 p.m. |
Created at: April 8, 2026, 9:57 p.m.