Triple
T14271432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alameda/Oakland–San Francisco route |
E353793
|
entity |
| Predicate | terminus |
P388
|
FINISHED |
| Object | Alameda |
E180395
|
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: Alameda | Statement: [Alameda/Oakland–San Francisco route, terminus, Alameda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alameda Context triple: [Alameda/Oakland–San Francisco route, terminus, Alameda]
-
A.
Alameda
Alameda is the main central avenue of Santiago, Chile, serving as a key thoroughfare and symbolic axis of the city.
-
B.
Alameda
Alameda is a major Lisbon metro and transport hub that serves as a key interchange point within the city's public transit network.
-
C.
Alameda, California
chosen
Alameda, California is a Bay Area island city adjacent to Oakland known for its historic Victorian architecture, waterfront parks, and residential neighborhoods.
-
D.
Santa Clara
Santa Clara is a Silicon Valley city in California known for its high-tech industry presence, Levi’s Stadium, and Santa Clara University.
-
E.
Santa Clara
Santa Clara is a settlement located within the Arraiján District in Panama.
- 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_69d8278d25148190abf1a8c8f5f533ad |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de65811d7c8190b075909a6570d415 |
completed | April 14, 2026, 4:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd6d7a13288190a73683e275f6bbc0 |
completed | May 8, 2026, 4:58 a.m. |
Created at: April 10, 2026, 1:10 a.m.