Triple
T13093733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kepulauan Sangihe Regency |
E310527
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object |
Tahuna
Tahuna is the main town and administrative center of the Sangihe Islands in North Sulawesi, Indonesia.
|
E1040026
|
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: Tahuna | Statement: [Kepulauan Sangihe Regency, capital, Tahuna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tahuna Context triple: [Kepulauan Sangihe Regency, capital, Tahuna]
-
A.
Balamban
Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
-
B.
Bucoda
Bucoda is a small town in Thurston County, Washington, known for its historic coal-mining roots and its claim as the "World's Tiniest Town with the Biggest Halloween Spirit."
-
C.
Tanauan
Tanauan is a city in the Calabarzon region of the Philippines known for its growing industrial zones and proximity to Metro Manila.
-
D.
Bamban
Bamban is a municipality in the province of Tarlac in the Philippines, known for its proximity to Mount Pinatubo and its role in the region’s post-eruption development and eco-tourism.
-
E.
Pagbilao
Pagbilao is a coastal municipality in the province of Quezon, Philippines, known for its power plant, beaches, and mangrove forests.
- 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: Tahuna Triple: [Kepulauan Sangihe Regency, capital, Tahuna]
Generated description
Tahuna is the main town and administrative center of the Sangihe Islands in North Sulawesi, Indonesia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tahuna Target entity description: Tahuna is the main town and administrative center of the Sangihe Islands in North Sulawesi, Indonesia.
-
A.
Balamban
Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
-
B.
Bucoda
Bucoda is a small town in Thurston County, Washington, known for its historic coal-mining roots and its claim as the "World's Tiniest Town with the Biggest Halloween Spirit."
-
C.
Tanauan
Tanauan is a city in the Calabarzon region of the Philippines known for its growing industrial zones and proximity to Metro Manila.
-
D.
Bamban
Bamban is a municipality in the province of Tarlac in the Philippines, known for its proximity to Mount Pinatubo and its role in the region’s post-eruption development and eco-tourism.
-
E.
Pagbilao
Pagbilao is a coastal municipality in the province of Quezon, Philippines, known for its power plant, beaches, and mangrove forests.
- 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_69d806a733548190989cfd4ce981ca33 |
completed | April 9, 2026, 8:05 p.m. |
| NER | Named-entity recognition | batch_69d9813cd1b881909871a318fdd60672 |
completed | April 10, 2026, 11:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7396e6cf881908b4cc3836501ed08 |
completed | May 3, 2026, 12:02 p.m. |
| NEDg | Description generation | batch_69f73a7ca9048190948c1bceede2a09c |
completed | May 3, 2026, 12:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f73abc1c9481909d509eb02bafd909 |
completed | May 3, 2026, 12:08 p.m. |
Created at: April 9, 2026, 9:03 p.m.