Triple
T4335019
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Calabarzon |
E97442
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | San Pablo |
E188577
|
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 Pablo | Statement: [Calabarzon, hasCity, San Pablo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Pablo Context triple: [Calabarzon, hasCity, San Pablo]
-
A.
San Pablo
chosen
San Pablo is a city in the province of Laguna in the Philippines, known for its seven crater lakes and role as a commercial and cultural hub in the Southern Tagalog region.
-
B.
Barueri
Barueri is a rapidly developing municipality in the São Paulo metropolitan area of Brazil, known for its strong commercial sector and high standard of living.
-
C.
Barretos
Barretos is a municipality in the Brazilian state of São Paulo, widely known for hosting one of the largest annual rodeo festivals in Latin America.
-
D.
Bauru
Bauru is a city in the state of São Paulo, Brazil, known as a regional economic and educational hub that hosts a campus of the University of São Paulo.
-
E.
Magé
Magé is a municipality in the state of Rio de Janeiro, Brazil, located in the metropolitan region of Rio de Janeiro and known for its coastal setting and historical significance.
- 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_69b3454662a481908fbcd0bbfaa3a0a4 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35152bfc88190ab5d53ca38f98d8a |
completed | March 12, 2026, 11:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5d0a9967481908828ceeb76ce4cbf |
completed | March 14, 2026, 9:18 p.m. |
Created at: March 12, 2026, 11:14 p.m.