Triple
T4264935
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chocolate Hills |
E96802
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Bohol |
E109173
|
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: Bohol | Statement: [Chocolate Hills, locatedIn, Bohol]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bohol Context triple: [Chocolate Hills, locatedIn, Bohol]
-
A.
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.
-
B.
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.
-
C.
Romblon
Romblon is an island province in the Philippines known for its marble industry, clear waters, and scenic beaches.
-
D.
Guimaras
Guimaras is a small island province in the Philippines known for its mango production, coastal scenery, and predominantly Hiligaynon-speaking population.
-
E.
Palawan
Palawan is a large island province in the western Philippines known for its stunning limestone cliffs, clear turquoise waters, rich marine biodiversity, and popular ecotourism destinations like El Nido and Puerto Princesa.
- 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_69b34543f06c8190915ebb1a4574ffa9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34fcad0a881908e1cac0a6da5a321 |
completed | March 12, 2026, 11:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5e4e3b6d8819099e8594dd9d0cd15 |
completed | March 14, 2026, 10:44 p.m. |
Created at: March 12, 2026, 11:06 p.m.