Triple
T12478180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Region I |
E298230
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Urdaneta
Urdaneta is a component city in the province of Pangasinan in the Philippines, known as a commercial and transportation hub in the Ilocos Region.
|
E984608
|
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: Urdaneta | Statement: [Region I, hasCity, Urdaneta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Urdaneta Context triple: [Region I, hasCity, Urdaneta]
-
A.
Urdaneta
Urdaneta is an upscale residential and commercial barangay in Makati, Metro Manila, known for its affluent neighborhoods and proximity to the city’s central business district.
-
B.
Danao
Danao is a coastal city and municipality on Cebu Island in the Philippines known for its historical significance and local industries.
-
C.
Plaridel
Plaridel is a municipality in the province of Bulacan in the Philippines, known for its historical significance and proximity to Metro Manila.
-
D.
Biellese
Biellese refers to people or things originating from Biella, a city in the Piedmont region of northern Italy known for its textile and wool industry.
-
E.
Paete
Paete is a lakeside municipality in the Philippine province of Laguna renowned for its skilled woodcarving and papier-mâché artisans.
- 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: Urdaneta Triple: [Region I, hasCity, Urdaneta]
Generated description
Urdaneta is a component city in the province of Pangasinan in the Philippines, known as a commercial and transportation hub in the Ilocos Region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Urdaneta Target entity description: Urdaneta is a component city in the province of Pangasinan in the Philippines, known as a commercial and transportation hub in the Ilocos Region.
-
A.
Urdaneta
Urdaneta is an upscale residential and commercial barangay in Makati, Metro Manila, known for its affluent neighborhoods and proximity to the city’s central business district.
-
B.
Danao
Danao is a coastal city and municipality on Cebu Island in the Philippines known for its historical significance and local industries.
-
C.
Plaridel
Plaridel is a municipality in the province of Bulacan in the Philippines, known for its historical significance and proximity to Metro Manila.
-
D.
Biellese
Biellese refers to people or things originating from Biella, a city in the Piedmont region of northern Italy known for its textile and wool industry.
-
E.
Paete
Paete is a lakeside municipality in the Philippine province of Laguna renowned for its skilled woodcarving and papier-mâché artisans.
- 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_69d6ada377208190a36011199a4d8558 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94dcc24e48190ae9c367a03f659f4 |
completed | April 10, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f63f2732a08190890493925e41a6e1 |
completed | May 2, 2026, 6:15 p.m. |
| NEDg | Description generation | batch_69f640b513488190893359e9964dbe98 |
completed | May 2, 2026, 6:21 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f641ad96fc819097430251a1cd71ff |
completed | May 2, 2026, 6:25 p.m. |
Created at: April 8, 2026, 9:56 p.m.