Triple
T15598667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Atherton |
E374971
|
entity |
| Predicate | nearbyEconomicCenters |
P36605
|
FINISHED |
| Object | San Jose |
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 Jose | Statement: [Atherton, nearbyEconomicCenters, San Jose]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Jose Context triple: [Atherton, nearbyEconomicCenters, San Jose]
-
A.
San Jose
chosen
San Jose is a major technology and innovation hub in Silicon Valley and one of the largest cities in Northern California.
-
B.
San Jose
San Jose is a coastal municipality in the Philippine province of Negros Oriental known for its rural communities and proximity to Dumaguete City.
-
C.
San Jose
San Jose is a coastal municipality in the province of Northern Samar in the Eastern Visayas region of the Philippines.
-
D.
San Jose
San Jose is a municipality in the province of Tarlac in the Central Luzon region of the Philippines, known for its predominantly agricultural economy.
-
E.
San Jose
San Jose is a municipality in the province of Batangas in the Philippines, known for its agricultural economy and rural communities.
- 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_69d85cce25008190b13b52745fbd719b |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e609ab081909feb486a57439960 |
completed | April 16, 2026, 2:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff82dcec0c8190a5e4ac0aafd34504 |
completed | May 9, 2026, 6:54 p.m. |
Created at: April 10, 2026, 4:12 a.m.