Triple
T800881
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luzon |
E17124
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
San Fernando
San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
|
E104726
|
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: [Luzon, containsCity, San Fernando]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Fernando Context triple: [Luzon, containsCity, San Fernando]
-
A.
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.
-
B.
San Fernando
San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
-
C.
Rosarito
Rosarito is a coastal resort city in northern Baja California, Mexico, known for its beaches, tourism, and proximity to the U.S. border.
-
D.
San Antonio de los Baños
San Antonio de los Baños is a Cuban town known for its film school and cultural traditions, located southwest of Havana.
-
E.
Serres
Serres is a historic city in northern Greece known for its Byzantine heritage and role as a regional economic and cultural 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: [Luzon, containsCity, San Fernando]
Generated description
San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: San Fernando Target entity description: San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
-
A.
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.
-
B.
San Fernando
San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
-
C.
Rosarito
Rosarito is a coastal resort city in northern Baja California, Mexico, known for its beaches, tourism, and proximity to the U.S. border.
-
D.
San Antonio de los Baños
San Antonio de los Baños is a Cuban town known for its film school and cultural traditions, located southwest of Havana.
-
E.
Serres
Serres is a historic city in northern Greece known for its Byzantine heritage and role as a regional economic and cultural center.
- 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_69a49378b9c48190adbf5f62e5b7aca1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a7cc75e88190bd35aabe51051b51 |
completed | March 1, 2026, 8:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7c00e3fb48190a242264358413b05 |
completed | March 4, 2026, 5:15 a.m. |
| NEDg | Description generation | batch_69a7c088506c81908a9e8ac91cb5b69d |
completed | March 4, 2026, 5:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a7c10b114c8190831a95519dd185a2 |
completed | March 4, 2026, 5:20 a.m. |
Created at: March 1, 2026, 7:38 p.m.