Triple
T4932944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Capiznon |
E110739
|
entity |
| Predicate | hasNeighborLanguage |
P16383
|
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: [Capiznon, hasNeighborLanguage, Aklanon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aklanon Context triple: [Capiznon, hasNeighborLanguage, 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.
Apayao
Apayao is a landlocked, mountainous province in the northern Philippines known for its rich indigenous culture, forests, and river systems.
-
D.
Talisay
Talisay is a city in the Philippine province of Negros Occidental known for its sugarcane industry and historical landmarks.
-
E.
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.
- 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_69bd4415190c8190817bee7ec9f9f944 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd70652d988190ba4a493db510952e |
completed | March 20, 2026, 4:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be923eac848190b8511b8027c87ff3 |
completed | March 21, 2026, 12:42 p.m. |
Created at: March 20, 2026, 1:30 p.m.