Triple
T14503607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pangasinan |
E340205
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Laoac
Laoac is a landlocked agricultural municipality in the province of Pangasinan in the Philippines.
|
E1230413
|
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: Laoac | Statement: [Pangasinan, hasMunicipality, Laoac]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laoac Context triple: [Pangasinan, hasMunicipality, Laoac]
-
A.
Tarlac City
Tarlac City is the capital and largest urban center of the province of Tarlac in the Central Luzon (Region III) area of the Philippines.
-
B.
Abucay
Abucay is a coastal municipality in the province of Bataan in the Philippines, known for its historical significance dating back to the Spanish colonial period.
-
C.
Laoag
Laoag is a coastal city in northern Luzon, Philippines, known as the capital of Ilocos Norte and a regional center for commerce, education, and tourism.
-
D.
Meycauayan
Meycauayan is a highly urbanized city in the Philippine province of Bulacan known for its jewelry and leather industries.
-
E.
Pasay City
Pasay City is a highly urbanized city in Metro Manila, Philippines, known for its major commercial centers, entertainment complexes, and proximity to the Ninoy Aquino International Airport.
- 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: Laoac Triple: [Pangasinan, hasMunicipality, Laoac]
Generated description
Laoac is a landlocked agricultural municipality in the province of Pangasinan in the Philippines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Laoac Target entity description: Laoac is a landlocked agricultural municipality in the province of Pangasinan in the Philippines.
-
A.
Tarlac City
Tarlac City is the capital and largest urban center of the province of Tarlac in the Central Luzon (Region III) area of the Philippines.
-
B.
Abucay
Abucay is a coastal municipality in the province of Bataan in the Philippines, known for its historical significance dating back to the Spanish colonial period.
-
C.
Laoag
Laoag is a coastal city in northern Luzon, Philippines, known as the capital of Ilocos Norte and a regional center for commerce, education, and tourism.
-
D.
Meycauayan
Meycauayan is a highly urbanized city in the Philippine province of Bulacan known for its jewelry and leather industries.
-
E.
Pasay City
Pasay City is a highly urbanized city in Metro Manila, Philippines, known for its major commercial centers, entertainment complexes, and proximity to the Ninoy Aquino International Airport.
- 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_69d822d9c0408190b9a2b3643e58bb4d |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69de94e0f9048190a2d266cfa4f9dfb6 |
completed | April 14, 2026, 7:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a009d20a42c819090319629544fa349 |
completed | May 10, 2026, 2:58 p.m. |
| NEDg | Description generation | batch_6a009e48cde08190aa7b569280a59d0e |
completed | May 10, 2026, 3:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a009ebf71608190bc1b3c063372d21b |
completed | May 10, 2026, 3:05 p.m. |
Created at: April 10, 2026, 1:21 a.m.