Triple
T4679790
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Diego Trolley |
E103770
|
entity |
| Predicate | connects |
P390
|
FINISHED |
| Object | San Ysidro |
E260565
|
NE FINISHED |
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 Ysidro | Statement: [San Diego Trolley, connects, San Ysidro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Ysidro Context triple: [San Diego Trolley, connects, San Ysidro]
-
A.
San Quintín
San Quintín is a coastal town in Baja California, Mexico, known for its agricultural production, volcanic landscapes, and growing tourism along the Pacific coast.
-
B.
Rosarito
Rosarito is a coastal resort city in northern Baja California, Mexico, known for its beaches, tourism, and proximity to the U.S. border.
-
C.
Santa Mesa
Santa Mesa is a historic district in Manila, Philippines, known for its role as a key battleground during the early stages of the Philippine–American War.
-
D.
Calipatria
Calipatria is a small city in Southern California’s Imperial Valley known for its agricultural economy and notably low elevation below sea level.
-
E.
San Ysidro, California
chosen
San Ysidro, California is a community in the southernmost part of San Diego, known for hosting one of the busiest land border crossings between the United States and Mexico.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69bd43dda32c8190938b37744ca270fc |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd636c105081908655ab384f539f38 |
completed | March 20, 2026, 3:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be105232e88190bb79bf52b58e1814 |
completed | March 21, 2026, 3:28 a.m. |
Created at: March 20, 2026, 1:16 p.m.