Triple
T9732864
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Region VI |
E235987
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Sagay |
E381139
|
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: Sagay | Statement: [Region VI, hasCity, Sagay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sagay Context triple: [Region VI, hasCity, Sagay]
-
A.
Sagay
Sagay is a coastal municipality on Camiguin Island in the Philippines known for its rural communities and access to beaches and marine resources.
-
B.
Sagay
chosen
Sagay is a coastal city in the province of Negros Occidental in the Philippines, known for its rich marine resources and protected seascape.
-
C.
Guihulngan
Guihulngan is a coastal city and commercial hub in the northern part of Negros Oriental in the Philippines.
-
D.
Bayugan
Bayugan is a component city in the Caraga region of Mindanao in the Philippines, known as an agricultural and commercial hub in its area.
-
E.
Dipaculao
Dipaculao is a coastal municipality in the Philippine province of Aurora known for its beaches, surfing spots, and scenic mountain landscapes.
- 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_69ca84d313e88190983ee6ffd0ef60d2 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9eb54fe481908b0202f104b75dc1 |
completed | April 1, 2026, 10:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d3170e01408190969fdd8c366f9276 |
completed | April 6, 2026, 2:14 a.m. |
Created at: March 30, 2026, 8:22 p.m.