Triple
T17286977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santa Clara County transportation system |
E419683
|
entity |
| Predicate | servesCity |
P82
|
FINISHED |
| Object | San Jose |
—
|
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 Jose | Statement: [Santa Clara County transportation system, servesCity, San Jose]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Jose Context triple: [Santa Clara County transportation system, servesCity, San Jose]
-
A.
San Jose
San Jose is a municipality in the province of Batangas in the Philippines, known for its agricultural economy and rural communities.
-
B.
San Jose
San Jose is a coastal municipality in the Philippine province of Romblon known for its island landscapes and fishing communities.
-
C.
San Jose
chosen
San Jose is a major technology and innovation hub in Silicon Valley and one of the largest cities in Northern California.
-
D.
San Jose
San Jose is a coastal municipality in the province of Northern Samar in the Eastern Visayas region of the Philippines.
-
E.
San Jose
San Jose is a municipality in the province of Tarlac in the Central Luzon region of the Philippines, known for its predominantly agricultural economy.
- 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_69d886db32608190a61e18862c5a8af6 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e4378024988190aac6aec006f8f7a1 |
completed | April 19, 2026, 2:01 a.m. |
Created at: April 10, 2026, 5:40 a.m.