Triple
T14011589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benguet |
E337091
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Mankayan
Mankayan is a mining town and municipality in the province of Benguet in the Cordillera region of the northern Philippines, known for its rich copper and gold deposits.
|
E1073033
|
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: Mankayan | Statement: [Benguet, contains, Mankayan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mankayan Context triple: [Benguet, contains, Mankayan]
-
A.
Banaybanay
Banaybanay is a municipality located in the province of Davao Oriental in the southeastern part of Mindanao, Philippines.
-
B.
Daang Matuwid
Daang Matuwid is the reform-focused governance platform and anti-corruption banner associated with the presidency of Benigno S. Aquino III in the Philippines.
-
C.
Mankanya
Mankanya is a Senegambian language spoken primarily by the Mankanya people in parts of Senegal, Gambia, and Guinea-Bissau.
-
D.
Hinunangan
Hinunangan is a coastal municipality in the province of Southern Leyte in the Philippines, known for its beaches and nearby island attractions.
-
E.
Bamanankan
Bamanankan is a Mande language widely spoken in Mali and neighboring West African countries, particularly by the Bambara people.
- 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: Mankayan Triple: [Benguet, contains, Mankayan]
Generated description
Mankayan is a mining town and municipality in the province of Benguet in the Cordillera region of the northern Philippines, known for its rich copper and gold deposits.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mankayan Target entity description: Mankayan is a mining town and municipality in the province of Benguet in the Cordillera region of the northern Philippines, known for its rich copper and gold deposits.
-
A.
Banaybanay
Banaybanay is a municipality located in the province of Davao Oriental in the southeastern part of Mindanao, Philippines.
-
B.
Daang Matuwid
Daang Matuwid is the reform-focused governance platform and anti-corruption banner associated with the presidency of Benigno S. Aquino III in the Philippines.
-
C.
Mankanya
Mankanya is a Senegambian language spoken primarily by the Mankanya people in parts of Senegal, Gambia, and Guinea-Bissau.
-
D.
Hinunangan
Hinunangan is a coastal municipality in the province of Southern Leyte in the Philippines, known for its beaches and nearby island attractions.
-
E.
Bamanankan
Bamanankan is a Mande language widely spoken in Mali and neighboring West African countries, particularly by the Bambara people.
- 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_69d81c645c5c8190b1fd16a285a1b78a |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2ed5cfd0819085b9c860b119a9de |
completed | April 14, 2026, 12:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbacaa16e88190995fd86951fb54e6 |
completed | May 6, 2026, 9:03 p.m. |
| NEDg | Description generation | batch_69fbada0a2408190b77d163aee17400e |
completed | May 6, 2026, 9:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fbaeeeb594819087b57da166495a72 |
completed | May 6, 2026, 9:13 p.m. |
Created at: April 9, 2026, 10:19 p.m.