Triple
T2133293
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Western Visayas |
E46591
|
entity |
| Predicate | majorLanguage |
P207
|
FINISHED |
| Object | Aklanon |
E104740
|
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: Aklanon | Statement: [Western Visayas, majorLanguage, Aklanon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aklanon Context triple: [Western Visayas, majorLanguage, Aklanon]
-
A.
Aklanon
chosen
Aklanon is an Austronesian language spoken primarily in the province of Aklan in the central Philippines.
-
B.
Aklan
Aklan is a province in the Philippines known for the world-famous Boracay Island and its vibrant Ati-Atihan Festival.
-
C.
Siquijor
Siquijor is a small island province in the central Philippines known for its white-sand beaches, coral reefs, and folklore surrounding mysticism and traditional healing.
-
D.
Aguiguan
Aguiguan is a small, uninhabited island in the Northern Mariana Islands known for its rugged terrain and seabird colonies.
-
E.
Guimaras
Guimaras is a small island province in the Philippines known for its mango production, coastal scenery, and predominantly Hiligaynon-speaking population.
- 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_69a88a1626548190ae59a5028c3baa8e |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abbba0c42c8190ab3ce4bbf1531ee1 |
completed | March 7, 2026, 5:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aea84f698081909e89bbeefd7d0894 |
completed | March 9, 2026, 11 a.m. |
Created at: March 4, 2026, 7:44 p.m.