Triple
T946843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Bay |
E20431
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Mountain View |
E29698
|
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: Mountain View | Statement: [South Bay, contains, Mountain View]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mountain View Context triple: [South Bay, contains, Mountain View]
-
A.
Mountain View
chosen
Mountain View is a Silicon Valley city in Northern California best known as a major technology hub and the home of companies like Google.
-
B.
Sunnyvale
Sunnyvale is a major Silicon Valley city in Northern California known for its high-tech industry presence and suburban residential communities.
-
C.
Sunnyvale
Sunnyvale is a suburban town in the Dallas–Fort Worth metropolitan area known for its residential character and proximity to Dallas, Texas.
-
D.
Los Altos Hills
Los Altos Hills is an affluent, primarily residential town in Northern California known for its large lots, rural character, and scenic views in the San Francisco Bay Area.
-
E.
San Bruno
San Bruno is a small city in San Mateo County, California, located just south of San Francisco and known for its proximity to San Francisco International Airport and the YouTube headquarters.
- 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_69a493b0f2fc81908cd227480a5356a1 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3bcad2481908b83575b2fb80d14 |
completed | March 1, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b31983cab08190afcd168864c9ccf2 |
completed | March 12, 2026, 7:52 p.m. |
Created at: March 1, 2026, 7:40 p.m.