Triple
T3770843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pampanga |
E83192
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Magalang
Magalang is a municipality in the Philippine province of Pampanga known for its agricultural lands and proximity to Mount Arayat.
|
E386372
|
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: Magalang | Statement: [Pampanga, hasMunicipality, Magalang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Magalang Context triple: [Pampanga, hasMunicipality, Magalang]
-
A.
Kabankalan
Kabankalan is a major inland city in the province of Negros Occidental in the Philippines, known as a commercial and agricultural hub in the southern part of the island.
-
B.
Balamban
Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
-
C.
Canlaon
Canlaon is a city in the Philippines known for its proximity to Mount Kanlaon, an active volcano and prominent natural landmark on Negros Island.
-
D.
Malungon
Malungon is a landlocked agricultural municipality in the province of South Cotabato in the Philippines, known for its hilly terrain and farming-based economy.
-
E.
Guihulngan
Guihulngan is a coastal city and commercial hub in the northern part of Negros Oriental in the 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: Magalang Triple: [Pampanga, hasMunicipality, Magalang]
Generated description
Magalang is a municipality in the Philippine province of Pampanga known for its agricultural lands and proximity to Mount Arayat.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Magalang Target entity description: Magalang is a municipality in the Philippine province of Pampanga known for its agricultural lands and proximity to Mount Arayat.
-
A.
Kabankalan
Kabankalan is a major inland city in the province of Negros Occidental in the Philippines, known as a commercial and agricultural hub in the southern part of the island.
-
B.
Balamban
Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
-
C.
Canlaon
Canlaon is a city in the Philippines known for its proximity to Mount Kanlaon, an active volcano and prominent natural landmark on Negros Island.
-
D.
Malungon
Malungon is a landlocked agricultural municipality in the province of South Cotabato in the Philippines, known for its hilly terrain and farming-based economy.
-
E.
Guihulngan
Guihulngan is a coastal city and commercial hub in the northern part of Negros Oriental in the 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_69ad8b235e608190b5a2b1d1bfcef50b |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcc307cf8819090730b5e697bb197 |
completed | March 8, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4e5287908819084319b8dfa407635 |
completed | March 14, 2026, 4:33 a.m. |
| NEDg | Description generation | batch_69b4e61c8dc881908e298528b1e42c0c |
completed | March 14, 2026, 4:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4e686bf2c8190aac01d6c1014c1d4 |
completed | March 14, 2026, 4:39 a.m. |
Created at: March 8, 2026, 3:36 p.m.