Triple
T20075616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alviso, San Jose |
E499854
|
entity |
| Predicate | annexedBy |
P960
|
FINISHED |
| Object | City of 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: City of San Jose | Statement: [Alviso, San Jose, annexedBy, City of San Jose]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: City of San Jose Context triple: [Alviso, San Jose, annexedBy, City of San Jose]
-
A.
San Jose City
San Jose City is a landlocked component city in the province of Nueva Ecija in the Philippines, known as an agricultural and commercial hub in Central Luzon.
-
B.
City of Santa Clara
The City of Santa Clara is a municipality in California’s Silicon Valley known for its technology companies, Levi’s Stadium, and proximity to major Bay Area hubs.
-
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_69da627770948190997f486f9a2e370f |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6643ab0448190ab18d013b72aaf32 |
completed | April 20, 2026, 5:36 p.m. |
Created at: April 11, 2026, 3:40 p.m.