Triple
T802124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mexican Alta California |
E17150
|
entity |
| Predicate | notableCity |
P2813
|
FINISHED |
| Object | San José |
E1776
|
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: San José | Statement: [Mexican Alta California, notableCity, San José]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San José Context triple: [Mexican Alta California, notableCity, San José]
-
A.
San José
San José is the capital and largest city of Costa Rica, known for its political, economic, and cultural significance in Central America.
-
B.
San Jose
chosen
San Jose is a major technology and innovation hub in Silicon Valley and one of the largest cities in Northern California.
-
C.
San Fernando
San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
-
D.
San Fernando
San Fernando is a major industrial and commercial city located in the southern part of Trinidad, known for its energy sector and bustling urban center.
-
E.
San Fernando
San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
- 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_69a49378b9c48190adbf5f62e5b7aca1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4aa9e0f0081909d2a89387d6c08e1 |
completed | March 1, 2026, 9:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad3ff724248190aacb72d105f0bb34 |
completed | March 8, 2026, 9:23 a.m. |
Created at: March 1, 2026, 7:38 p.m.