Triple
T15941496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Larecaja Province |
E386574
|
entity |
| Predicate | seat |
P75
|
FINISHED |
| Object | Sorata |
E1184658
|
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: Sorata | Statement: [Larecaja Province, seat, Sorata]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sorata Context triple: [Larecaja Province, seat, Sorata]
-
A.
Sorata
chosen
Sorata is a picturesque town in Bolivia’s Andes, known as a gateway to trekking in the Cordillera Real and for its mild climate and scenic mountain views.
-
B.
Ibusuki
Ibusuki is a coastal city in Kagoshima Prefecture, Japan, best known for its natural hot springs and unique sand bath spas.
-
C.
Saijo
Saijo is a city in Ehime Prefecture on Japan’s Shikoku island, known for its industrial facilities and maritime-related industries.
-
D.
Tsurugizaki
Tsurugizaki was the original name of the Imperial Japanese Navy vessel that was later converted into and commissioned as the light aircraft carrier Shōhō during World War II.
-
E.
Akiruno
Akiruno is a city in western Tokyo, Japan, known for its natural scenery, including rivers, forests, and hiking areas.
- 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_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156ce0230819089a20114a755a75a |
completed | April 16, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffbe7455c48190bfad24eb8905426d |
completed | May 9, 2026, 11:08 p.m. |
Created at: April 10, 2026, 4:53 a.m.