Triple
T14596885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Culaba |
E342592
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Biliran province |
E342589
|
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: Biliran province | Statement: [Culaba, partOf, Biliran province]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Biliran province Context triple: [Culaba, partOf, Biliran province]
-
A.
Biliran Province
chosen
Biliran Province is a small island province in the Eastern Visayas region of the Philippines, known for its volcanic landscapes, waterfalls, and coastal scenery.
-
B.
Biliran
Biliran is an island province in the central Philippines known for its volcanic landscapes, waterfalls, and coastal scenery.
-
C.
Simbu Province
Simbu Province is a mountainous inland province in central Papua New Guinea known for its rugged terrain, highland cultures, and traditional agricultural communities.
-
D.
Madang Province
Madang Province is a coastal region of Papua New Guinea known for its linguistic diversity, including the Bel languages, and its rich marine and mountainous landscapes.
-
E.
Luya Province
Luya Province is an administrative province in northern Peru known for its archaeological sites, cloud forests, and location within the Amazonas Region.
- 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_69d822ddc0f081909cd8163c7de298cd |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb43581348190b5362251c3a89654 |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe729f894881908647da558bef1031 |
completed | May 8, 2026, 11:32 p.m. |
Created at: April 10, 2026, 1:25 a.m.