Triple
T9652635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southern Leyte |
E233372
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Anahawan
Anahawan is a coastal municipality in the province of Southern Leyte in the Philippines, known for its rural communities and agricultural economy.
|
E813475
|
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: Anahawan | Statement: [Southern Leyte, hasMunicipality, Anahawan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anahawan Context triple: [Southern Leyte, hasMunicipality, Anahawan]
-
A.
Tanauan
Tanauan is a city in the Calabarzon region of the Philippines known for its growing industrial zones and proximity to Metro Manila.
-
B.
Maljamar
Maljamar is a small unincorporated community in southeastern New Mexico known historically for its oil and gas activity.
-
C.
Pigcawayan
Pigcawayan is a municipality in the province of North Cotabato in the Philippines, known for its predominantly agricultural economy and rural communities.
-
D.
Calatagan
Calatagan is a coastal municipality in the province of Batangas in the Philippines, known for its beaches, diving spots, and historical sites.
-
E.
Tubigon
Tubigon is a coastal municipality in the Philippine province of Bohol, known as a busy port town and gateway to nearby islands such as Cebu.
- 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: Anahawan Triple: [Southern Leyte, hasMunicipality, Anahawan]
Generated description
Anahawan is a coastal municipality in the province of Southern Leyte in the Philippines, known for its rural communities and agricultural economy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anahawan Target entity description: Anahawan is a coastal municipality in the province of Southern Leyte in the Philippines, known for its rural communities and agricultural economy.
-
A.
Tanauan
Tanauan is a city in the Calabarzon region of the Philippines known for its growing industrial zones and proximity to Metro Manila.
-
B.
Maljamar
Maljamar is a small unincorporated community in southeastern New Mexico known historically for its oil and gas activity.
-
C.
Pigcawayan
Pigcawayan is a municipality in the province of North Cotabato in the Philippines, known for its predominantly agricultural economy and rural communities.
-
D.
Calatagan
Calatagan is a coastal municipality in the province of Batangas in the Philippines, known for its beaches, diving spots, and historical sites.
-
E.
Tubigon
Tubigon is a coastal municipality in the Philippine province of Bohol, known as a busy port town and gateway to nearby islands such as Cebu.
- 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_69ca848b31648190b57aa55da20285be |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9bb26b748190bc32e2003829b0ec |
completed | April 1, 2026, 10:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d18a03438c8190a3419cbed7af4cd4 |
completed | April 4, 2026, 10 p.m. |
| NEDg | Description generation | batch_69d18a7f4074819086316d0236f09e0b |
completed | April 4, 2026, 10:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d18af220808190b91eb750f2939e4e |
completed | April 4, 2026, 10:04 p.m. |
Created at: March 30, 2026, 8:13 p.m.