Triple
T13980638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kamayo |
E336299
|
entity |
| Predicate | spokenIn |
P2266
|
FINISHED |
| Object | Hinatuan |
E959223
|
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: Hinatuan | Statement: [Kamayo, spokenIn, Hinatuan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hinatuan Context triple: [Kamayo, spokenIn, Hinatuan]
-
A.
Hinatuan
chosen
Hinatuan is a coastal municipality in the province of Surigao del Sur in the Philippines, best known for its clear blue Hinatuan Enchanted River.
-
B.
Lambuyao
Lambuyao is a barangay (village-level administrative division) within the municipality of Oton in the province of Iloilo, Philippines.
-
C.
Balamban
Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
-
D.
Tanauan
Tanauan is a city in the Calabarzon region of the Philippines known for its growing industrial zones and proximity to Metro Manila.
-
E.
Sarangani
Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
- 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_69d81c639e808190a0e4b4f3d31c6a59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2ea10dc88190b9720919a021e570 |
completed | April 14, 2026, 12:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbc323d8ac81909b4eaf44f8fd462c |
completed | May 6, 2026, 10:39 p.m. |
Created at: April 9, 2026, 10:18 p.m.