Triple
T6275916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles W. Davidson College of Engineering |
E140657
|
entity |
| Predicate | city |
P40
|
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: [Charles W. Davidson College of Engineering, city, San José]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San José Context triple: [Charles W. Davidson College of Engineering, city, 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 José
San José is a small municipality and town located in the Caldas Department of Colombia, known for its coffee-growing rural landscape in the Andean region.
-
C.
San Jose City
San Jose City is a landlocked component city in the province of Nueva Ecija in the Philippines, known as an agricultural and commercial hub in Central Luzon.
-
D.
San Jose
San Jose is the main town on the island of Tinian in the Northern Mariana Islands, serving as its administrative and population center.
-
E.
San Jose
chosen
San Jose is a major technology and innovation hub in Silicon Valley and one of the largest cities in Northern California.
- 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_69c008cc158881908df6ec94a911c736 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063c170bc8190933e2fd5c9fef783 |
completed | March 22, 2026, 9:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c65fb7e7c88190bef0a15c12250b13 |
completed | March 27, 2026, 10:45 a.m. |
Created at: March 22, 2026, 4:25 p.m.