Triple
T10078171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benicia |
E213826
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Vallejo |
E28332
|
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: Vallejo | Statement: [Benicia, locatedNear, Vallejo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vallejo Context triple: [Benicia, locatedNear, Vallejo]
-
A.
Vallejo
chosen
Vallejo is a waterfront city in the San Francisco Bay Area known for its former Mare Island Naval Shipyard and diverse, working-class community.
-
B.
Vallejo
Vallejo is a metro station in Mexico City that serves passengers on Line 6 of the city’s rapid transit system.
-
C.
San Jose Diridon
San Jose Diridon is a major intermodal transit hub in San Jose, California, serving Amtrak, commuter rail, light rail, and bus services.
-
D.
Santa Cruz
Santa Cruz is a coastal municipality in the Philippine island province of Marinduque known for its fishing communities and rural island-barangays.
-
E.
Santa Cruz
Santa Cruz is a notable wine-producing city in central Chile’s Colchagua Valley, recognized for its vineyards, tourism, and colonial charm.
- 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_69ca839bf730819086900c323c9b8c95 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd030a0fc819084b523e8e63636fa |
completed | April 2, 2026, 2:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d29ad05bbc8190b103d66c9e786c86 |
completed | April 5, 2026, 5:24 p.m. |
Created at: March 30, 2026, 9 p.m.