Triple
T5096394
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boracay |
E114875
|
entity |
| Predicate | hasProvince |
P285
|
FINISHED |
| Object | Aklan |
E250504
|
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: Aklan | Statement: [Boracay, hasProvince, Aklan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aklan Context triple: [Boracay, hasProvince, Aklan]
-
A.
Aklan
chosen
Aklan is a province in the Philippines known for the world-famous Boracay Island and its vibrant Ati-Atihan Festival.
-
B.
Guimaras
Guimaras is a small island province in the Philippines known for its mango production, coastal scenery, and predominantly Hiligaynon-speaking population.
-
C.
Aklanon
Aklanon is an Austronesian language spoken primarily in the province of Aklan in the central Philippines.
-
D.
Apayao
Apayao is a landlocked, mountainous province in the northern Philippines known for its rich indigenous culture, forests, and river systems.
-
E.
Iloilo province
Iloilo province is a province in the Western Visayas region of the Philippines known for its rich cultural heritage, historic churches, and vibrant coastal and agricultural communities.
- 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_69bd443fc49c819089629c00e311310c |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd75652a8081908386718f1fdb1de3 |
completed | March 20, 2026, 4:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c059bd73e481909e23e1796262b8c4 |
completed | March 22, 2026, 9:06 p.m. |
Created at: March 20, 2026, 1:40 p.m.