Triple
T22424540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siparia |
E554334
|
entity |
| Predicate | roadConnection |
P385
|
FINISHED |
| Object | San Fernando |
—
|
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: San Fernando | Statement: [Siparia, roadConnection, San Fernando]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Fernando Context triple: [Siparia, roadConnection, San Fernando]
-
A.
San Fernando
chosen
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.
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.
-
D.
San Fernando
San Fernando is a small independent city in Los Angeles County, California, surrounded by but administratively separate from the San Fernando Valley region of Los Angeles.
-
E.
San Fernando
San Fernando is a municipality located in the Morazán Department of northeastern El Salvador, known for its rural character and mountainous surroundings.
- 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_69e11e4f2d0c819091aa3558ea2ee630 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15a2c47bc81908b8265d83fa6fb65 |
completed | April 29, 2026, 1:09 a.m. |
Created at: April 16, 2026, 8:47 p.m.