Triple
T3199673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Bikol |
E67019
|
entity |
| Predicate | spokenIn |
P2266
|
FINISHED |
| Object |
Catanduanes
Catanduanes is an island province in the Bicol Region of the Philippines known for its rugged coastlines, surfing beaches, and predominantly Bikol-speaking population.
|
E342274
|
NE FINISHED |
How this triple was built (4 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: Catanduanes | Statement: [Central Bikol, spokenIn, Catanduanes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Catanduanes Context triple: [Central Bikol, spokenIn, Catanduanes]
-
A.
Marinduque
Marinduque is an island province in the Philippines known for its heart-shaped geography and the annual Moriones Festival.
-
B.
Albay
Albay is a province in the Bicol Region of the Philippines, known for the iconic Mayon Volcano and its rich Bikolano culture.
-
C.
Zambales
Zambales is a coastal province in the Central Luzon region of the Philippines, known for its beaches, mangoes, and ethnolinguistic diversity.
-
D.
Siquijor
Siquijor is a small island province in the central Philippines known for its white-sand beaches, coral reefs, and folklore surrounding mysticism and traditional healing.
-
E.
Romblon
Romblon is an island province in the Philippines known for its marble industry, clear waters, and scenic beaches.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Catanduanes Triple: [Central Bikol, spokenIn, Catanduanes]
Generated description
Catanduanes is an island province in the Bicol Region of the Philippines known for its rugged coastlines, surfing beaches, and predominantly Bikol-speaking population.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Catanduanes Target entity description: Catanduanes is an island province in the Bicol Region of the Philippines known for its rugged coastlines, surfing beaches, and predominantly Bikol-speaking population.
-
A.
Marinduque
Marinduque is an island province in the Philippines known for its heart-shaped geography and the annual Moriones Festival.
-
B.
Albay
Albay is a province in the Bicol Region of the Philippines, known for the iconic Mayon Volcano and its rich Bikolano culture.
-
C.
Zambales
Zambales is a coastal province in the Central Luzon region of the Philippines, known for its beaches, mangoes, and ethnolinguistic diversity.
-
D.
Siquijor
Siquijor is a small island province in the central Philippines known for its white-sand beaches, coral reefs, and folklore surrounding mysticism and traditional healing.
-
E.
Romblon
Romblon is an island province in the Philippines known for its marble industry, clear waters, and scenic beaches.
- F. None of above. chosen
Provenance (5 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_69ad8589bd988190afa7ed2bdffb7b33 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada9ad4b1c8190bc6ad0f025f238c8 |
completed | March 8, 2026, 4:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b28e69ff6081908189e2e756e3748b |
completed | March 12, 2026, 9:59 a.m. |
| NEDg | Description generation | batch_69b28f9e12488190b93355b783300264 |
completed | March 12, 2026, 10:04 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b2c092063481909982dea3f71c00c1 |
completed | March 12, 2026, 1:33 p.m. |
Created at: March 8, 2026, 3:07 p.m.