Triple
T6099212
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Isnag language |
E135951
|
entity |
| Predicate | hasDialects |
P4251
|
FINISHED |
| Object |
Karagawan
Karagawan is a regional dialect of the Isnag language spoken by the Isnag people of northern Luzon in the Philippines.
|
E569541
|
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: Karagawan | Statement: [Isnag language, hasDialects, Karagawan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karagawan Context triple: [Isnag language, hasDialects, Karagawan]
-
A.
Sarangani
Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
-
B.
Danao
Danao is a coastal city and municipality on Cebu Island in the Philippines known for its historical significance and local industries.
-
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.
Maragondon
Maragondon is a historic rural municipality in the province of Cavite in the Philippines, known for its Spanish-era heritage sites and nearby natural attractions.
- 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: Karagawan Triple: [Isnag language, hasDialects, Karagawan]
Generated description
Karagawan is a regional dialect of the Isnag language spoken by the Isnag people of northern Luzon in the Philippines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Karagawan Target entity description: Karagawan is a regional dialect of the Isnag language spoken by the Isnag people of northern Luzon in the Philippines.
-
A.
Sarangani
Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
-
B.
Danao
Danao is a coastal city and municipality on Cebu Island in the Philippines known for its historical significance and local industries.
-
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.
Maragondon
Maragondon is a historic rural municipality in the province of Cavite in the Philippines, known for its Spanish-era heritage sites and nearby natural attractions.
- 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_69c0087cd3c48190b459848c72d84eb1 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05a9a02888190ac201acd14c3fc31 |
completed | March 22, 2026, 9:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c125475548819086b733a80056eba5 |
completed | March 23, 2026, 11:34 a.m. |
| NEDg | Description generation | batch_69c128753cd8819096edb3c817bfae10 |
completed | March 23, 2026, 11:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c129134ce08190ada54a7b3eda27f4 |
completed | March 23, 2026, 11:50 a.m. |
Created at: March 22, 2026, 4:13 p.m.