Triple
T10632976
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aklan |
E250504
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Madalag
Madalag is a rural municipality in the province of Aklan in the Philippines, known for its mountainous terrain and river landscapes.
|
E876382
|
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: Madalag | Statement: [Aklan, hasMunicipality, Madalag]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Madalag Context triple: [Aklan, hasMunicipality, Madalag]
-
A.
Dumalag
Dumalag is a municipality in the province of Capiz in the Western Visayas region of the Philippines, known for its rural landscapes and small-town character.
-
B.
Maydolong
Maydolong is a coastal municipality in the province of Eastern Samar in the Philippines, known for its rural communities and Pacific shoreline.
-
C.
Malaun
Malaun is a historic hill town and former fortress area in Himachal Pradesh, India, known for its strategic role in early 19th-century Anglo-Gurkha conflicts.
-
D.
Mangilao
Mangilao is a village on the eastern side of Guam known for hosting the University of Guam and Guam Community College.
-
E.
Tamasopo
Tamasopo is a small town in the Huasteca Potosina region of San Luis Potosí, Mexico, known for its lush landscapes and popular nearby waterfalls and natural swimming areas.
- 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: Madalag Triple: [Aklan, hasMunicipality, Madalag]
Generated description
Madalag is a rural municipality in the province of Aklan in the Philippines, known for its mountainous terrain and river landscapes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Madalag Target entity description: Madalag is a rural municipality in the province of Aklan in the Philippines, known for its mountainous terrain and river landscapes.
-
A.
Dumalag
Dumalag is a municipality in the province of Capiz in the Western Visayas region of the Philippines, known for its rural landscapes and small-town character.
-
B.
Maydolong
Maydolong is a coastal municipality in the province of Eastern Samar in the Philippines, known for its rural communities and Pacific shoreline.
-
C.
Malaun
Malaun is a historic hill town and former fortress area in Himachal Pradesh, India, known for its strategic role in early 19th-century Anglo-Gurkha conflicts.
-
D.
Mangilao
Mangilao is a village on the eastern side of Guam known for hosting the University of Guam and Guam Community College.
-
E.
Tamasopo
Tamasopo is a small town in the Huasteca Potosina region of San Luis Potosí, Mexico, known for its lush landscapes and popular nearby waterfalls and natural swimming areas.
- 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_69d6aa5993448190a493b790b8f85010 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6df95f5e88190b34ce3ec972759ef |
completed | April 8, 2026, 11:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d96bbd64d8819089d55af875d39e45 |
completed | April 10, 2026, 9:29 p.m. |
| NEDg | Description generation | batch_69d9701de92881908c0b8f05eae97e35 |
completed | April 10, 2026, 9:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d970f3f78081909bcb2dae6dae06d5 |
completed | April 10, 2026, 9:51 p.m. |
Created at: April 8, 2026, 9:02 p.m.