Triple
T14011585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benguet |
E337091
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Kabayan
Kabayan is a mountainous municipality in the Philippine province of Benguet known for its rice terraces, scenic highland landscapes, and ancient mummified remains.
|
E1073031
|
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: Kabayan | Statement: [Benguet, contains, Kabayan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kabayan Context triple: [Benguet, contains, Kabayan]
-
A.
Kabugao
Kabugao is a dialect of the Isnag language spoken by indigenous communities in the northern Philippines.
-
B.
Kabugao
Kabugao is a municipality in the Philippines that serves as the administrative and political center of the province of Apayao.
-
C.
Kabuntalan
Kabuntalan is a municipality in the province of Maguindanao del Norte in the Philippines, known for its location along the Rio Grande de Mindanao and its predominantly Maguindanaon population.
-
D.
Kawkaban
Kawkaban is a historic fortified mountain town in Yemen renowned for its strategic clifftop location and traditional architecture.
-
E.
Lambuyao
Lambuyao is a barangay (village-level administrative division) within the municipality of Oton in the province of Iloilo, Philippines.
- 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: Kabayan Triple: [Benguet, contains, Kabayan]
Generated description
Kabayan is a mountainous municipality in the Philippine province of Benguet known for its rice terraces, scenic highland landscapes, and ancient mummified remains.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kabayan Target entity description: Kabayan is a mountainous municipality in the Philippine province of Benguet known for its rice terraces, scenic highland landscapes, and ancient mummified remains.
-
A.
Kabugao
Kabugao is a dialect of the Isnag language spoken by indigenous communities in the northern Philippines.
-
B.
Kabugao
Kabugao is a municipality in the Philippines that serves as the administrative and political center of the province of Apayao.
-
C.
Kabuntalan
Kabuntalan is a municipality in the province of Maguindanao del Norte in the Philippines, known for its location along the Rio Grande de Mindanao and its predominantly Maguindanaon population.
-
D.
Kawkaban
Kawkaban is a historic fortified mountain town in Yemen renowned for its strategic clifftop location and traditional architecture.
-
E.
Lambuyao
Lambuyao is a barangay (village-level administrative division) within the municipality of Oton in the province of Iloilo, Philippines.
- 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.