Triple
T2375891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cebu |
E46197
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Balamban
Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
|
E332777
|
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: Balamban | Statement: [Cebu, hasPart, Balamban]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Balamban Context triple: [Cebu, hasPart, Balamban]
-
A.
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.
-
B.
Moalboal
Moalboal is a coastal town in the Philippines renowned for its vibrant coral reefs, sardine runs, and popular diving and snorkeling spots.
-
C.
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.
-
D.
Sarangani
Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
-
E.
Tacurong
Tacurong is a component city in the Philippines located in the Soccsksargen region on the island of Mindanao.
- 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: Balamban Triple: [Cebu, hasPart, Balamban]
Generated description
Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Balamban Target entity description: Balamban is a coastal municipality in the province of Cebu in the Philippines, known for its shipbuilding industry and growing economic zone.
-
A.
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.
-
B.
Moalboal
Moalboal is a coastal town in the Philippines renowned for its vibrant coral reefs, sardine runs, and popular diving and snorkeling spots.
-
C.
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.
-
D.
Sarangani
Sarangani is a coastal province in the southern Philippines known for its rich marine biodiversity, tuna industry, and diverse indigenous cultures.
-
E.
Tacurong
Tacurong is a component city in the Philippines located in the Soccsksargen region on the island of Mindanao.
- 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_69a88a1554a48190a0180682bcf099be |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abc794eee481908163148e1e666d9b |
completed | March 7, 2026, 6:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2356752c08190aac1b36f6c14ad8c |
completed | March 12, 2026, 3:39 a.m. |
| NEDg | Description generation | batch_69b236962ee48190b37836e5fe6dbc37 |
completed | March 12, 2026, 3:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b237397e14819093a7192d28c59ad1 |
completed | March 12, 2026, 3:47 a.m. |
Created at: March 4, 2026, 7:57 p.m.