Triple
T20025522
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Province of Leyte |
E494972
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Tanauan
Tanauan is a coastal municipality in the province of Leyte in the Philippines, known for its fishing communities and proximity to Tacloban City.
|
E1410407
|
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: Tanauan | Statement: [Province of Leyte, hasMunicipality, Tanauan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tanauan Context triple: [Province of Leyte, hasMunicipality, Tanauan]
-
A.
Tanauan
Tanauan is a city in the Calabarzon region of the Philippines known for its growing industrial zones and proximity to Metro Manila.
-
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.
Calatagan
Calatagan is a coastal municipality in the province of Batangas in the Philippines, known for its beaches, diving spots, and historical sites.
-
D.
Bayuyungan
Bayuyungan is the former name of the municipality now known as Laurel in the province of Batangas, Philippines.
-
E.
Balayan
Balayan is a historic coastal municipality in the province of Batangas in the Philippines, known for its heritage houses and annual Parada ng Lechon festival.
- 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: Tanauan Triple: [Province of Leyte, hasMunicipality, Tanauan]
Generated description
Tanauan is a coastal municipality in the province of Leyte in the Philippines, known for its fishing communities and proximity to Tacloban City.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tanauan Target entity description: Tanauan is a coastal municipality in the province of Leyte in the Philippines, known for its fishing communities and proximity to Tacloban City.
-
A.
Tanauan
Tanauan is a city in the Calabarzon region of the Philippines known for its growing industrial zones and proximity to Metro Manila.
-
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.
Calatagan
Calatagan is a coastal municipality in the province of Batangas in the Philippines, known for its beaches, diving spots, and historical sites.
-
D.
Bayuyungan
Bayuyungan is the former name of the municipality now known as Laurel in the province of Batangas, Philippines.
-
E.
Balayan
Balayan is a historic coastal municipality in the province of Batangas in the Philippines, known for its heritage houses and annual Parada ng Lechon festival.
- 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_69da626bfd288190aa5d65098b6433ae |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6628d5b8c8190a35f95ac4a016550 |
completed | April 20, 2026, 5:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a081f2b9e908190820c5bb3bf8bb28c |
completed | May 16, 2026, 7:39 a.m. |
| NEDg | Description generation | batch_6a08201f36b88190b3c80942de463d36 |
completed | May 16, 2026, 7:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08208c203c819083abea34d10d5e4e |
completed | May 16, 2026, 7:45 a.m. |
Created at: April 11, 2026, 3:35 p.m.