Triple
T17508616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naval School of San Fernando |
E426390
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
San Fernando
San Fernando is a coastal city in the Province of Cádiz, Andalusia, Spain, historically significant as a naval and military center.
|
E184740
|
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: San Fernando | Statement: [Naval School of San Fernando, namedAfter, San Fernando]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Fernando Context triple: [Naval School of San Fernando, namedAfter, San Fernando]
-
A.
San Fernando
San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
-
B.
San Fernando
San Fernando is a locality within the municipality of Huixquilucan in the State of Mexico, forming part of the greater Mexico City metropolitan area.
-
C.
San Fernando
San Fernando is a coastal municipality located in the island province of Romblon in the Philippines.
-
D.
San Fernando
San Fernando is a coastal municipality in the province of Cebu in the Philippines, situated within the greater Metro Cebu area.
-
E.
San Fernando
San Fernando is a major industrial and commercial city located in the southern part of Trinidad, known for its energy sector and bustling urban center.
- 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: San Fernando Triple: [Naval School of San Fernando, namedAfter, San Fernando]
Generated description
San Fernando is a coastal city in the Province of Cádiz, Andalusia, Spain, historically significant as a naval and military center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: San Fernando Target entity description: San Fernando is a coastal city in the Province of Cádiz, Andalusia, Spain, historically significant as a naval and military center.
-
A.
San Fernando
chosen
San Fernando is a coastal city in the Province of Cádiz, Andalusia, Spain, known for its naval base, salt marshes, and historical role in the Spanish War of Independence.
-
B.
San Fernando
San Fernando is a coastal municipality in the province of Cebu in the Philippines, situated within the greater Metro Cebu area.
-
C.
San Fernando
San Fernando is a coastal municipality located in the island province of Romblon in the Philippines.
-
D.
San Fernando
San Fernando is a coastal municipality in the Philippine province of Masbate, known for its rural communities and fishing-based local economy.
-
E.
San Fernando
San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
- F. None of above.
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_69d889dd9164819087b1dc3c9240c870 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4525985f881909a5ad28b30762680 |
completed | April 19, 2026, 3:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01c93b1b348190a583d0da5ed4e90b |
completed | May 11, 2026, 12:19 p.m. |
| NEDg | Description generation | batch_6a01cb066c748190953c59b2fcc19751 |
completed | May 11, 2026, 12:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01cb9740b881909eafe1461632890e |
completed | May 11, 2026, 12:29 p.m. |
Created at: April 10, 2026, 5:48 a.m.