Triple
T21510502
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vuelta Abajo tobacco-growing region |
E530704
|
entity |
| Predicate | hasSubregion |
P285
|
FINISHED |
| Object | San Luis |
—
|
NE NERFINISHED |
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 Luis | Statement: [Vuelta Abajo tobacco-growing region, hasSubregion, San Luis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Luis Context triple: [Vuelta Abajo tobacco-growing region, hasSubregion, San Luis]
-
A.
San Luis
San Luis is a municipal barrio (district) of the mountainous town of Aibonito in central Puerto Rico.
-
B.
San Luis
San Luis is a residential and commercial district located in the eastern part of Lima, Peru.
-
C.
San Luis
chosen
San Luis is a municipality and town in western Cuba known for its agricultural activities within Pinar del Río Province.
-
D.
San Luis
San Luis is a province in central Argentina known for its mountainous landscapes, arid climate, and role in the country’s early independence era.
-
E.
San Luis
San Luis is a landlocked agricultural municipality in the province of Pampanga in the Philippines, known for its rice fields and rural communities.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c45c81f08190a6b8bbb70a45aae7 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9ea84dfbc8190a23d9a7d6eb2c2b5 |
completed | April 23, 2026, 9:46 a.m. |
Created at: April 16, 2026, 6:25 p.m.