Triple
T10078172
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benicia |
E213826
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Martinez |
E93615
|
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: Martinez | Statement: [Benicia, locatedNear, Martinez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martinez Context triple: [Benicia, locatedNear, Martinez]
-
A.
Martinez
Martinez is a common Spanish-origin surname widely borne across the Spanish-speaking world and beyond.
-
B.
Martinez, California
chosen
Martinez, California is a historic waterfront city in the San Francisco Bay Area known as the county seat of Contra Costa County and for its role as a regional rail and transportation hub.
-
C.
Vallejo
Vallejo is a waterfront city in the San Francisco Bay Area known for its former Mare Island Naval Shipyard and diverse, working-class community.
-
D.
Vallejo
Vallejo is a metro station in Mexico City that serves passengers on Line 6 of the city’s rapid transit system.
-
E.
Santa Mesa
Santa Mesa is a historic district in Manila, Philippines, known for its role as a key battleground during the early stages of the Philippine–American War.
- 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.