Triple
T20283102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tagbilaran Airport |
E503199
|
entity |
| Predicate | servedIsland |
P60499
|
FINISHED |
| Object | Bohol |
—
|
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: Bohol | Statement: [Tagbilaran Airport, servedIsland, Bohol]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bohol Context triple: [Tagbilaran Airport, servedIsland, Bohol]
-
A.
Bohol Province
Bohol Province is a popular island province in the central Philippines known for its Chocolate Hills, tarsier sanctuaries, white-sand beaches, and rich cultural heritage.
-
B.
Bohol Island
chosen
Bohol Island is a popular island province in the central Philippines known for its Chocolate Hills, tarsier sanctuaries, and white-sand beaches.
-
C.
Leyte
Leyte is a large island province in the Eastern Visayas region of the Philippines, known for its rich cultural traditions and historical significance, including major World War II events.
-
D.
Romblon
Romblon is an island province in the Philippines known for its marble industry, clear waters, and scenic beaches.
-
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 (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_69e0b4b0e79c8190bd61f22ef1329fa8 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6769049b48190bc449557b79b9e81 |
completed | April 20, 2026, 6:55 p.m. |
Created at: April 16, 2026, 10:40 a.m.