Triple
T14969795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Argao |
E373285
|
entity |
| Predicate | hasBarangay |
P29835
|
FINISHED |
| Object |
Binlod
Binlod is a rural barangay (village-level administrative division) of the municipality of Argao in the province of Cebu, Philippines.
|
E1129962
|
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: Binlod | Statement: [Argao, hasBarangay, Binlod]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Binlod Context triple: [Argao, hasBarangay, Binlod]
-
A.
Buruanga
Buruanga is a coastal municipality in the province of Aklan in the Philippines, known for its scenic beaches and proximity to the tourist island of Boracay.
-
B.
Binalong
Binalong is a small rural village in New South Wales, Australia, known for its historic buildings and pastoral surroundings.
-
C.
Tabogon
Tabogon is a coastal municipality in the province of Cebu in the Philippines, known for its agricultural lands and scenic seaside areas.
-
D.
Binongko
Binongko is an island in Indonesia’s Wakatobi archipelago, known for its traditional blacksmithing culture and remote, rugged coastal landscapes.
-
E.
Balungao
Balungao is a landlocked agricultural municipality in the province of Pangasinan in the Philippines, known for its hilly terrain and hot and cold springs.
- 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: Binlod Triple: [Argao, hasBarangay, Binlod]
Generated description
Binlod is a rural barangay (village-level administrative division) of the municipality of Argao in the province of Cebu, Philippines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Binlod Target entity description: Binlod is a rural barangay (village-level administrative division) of the municipality of Argao in the province of Cebu, Philippines.
-
A.
Buruanga
Buruanga is a coastal municipality in the province of Aklan in the Philippines, known for its scenic beaches and proximity to the tourist island of Boracay.
-
B.
Binalong
Binalong is a small rural village in New South Wales, Australia, known for its historic buildings and pastoral surroundings.
-
C.
Tabogon
Tabogon is a coastal municipality in the province of Cebu in the Philippines, known for its agricultural lands and scenic seaside areas.
-
D.
Binongko
Binongko is an island in Indonesia’s Wakatobi archipelago, known for its traditional blacksmithing culture and remote, rugged coastal landscapes.
-
E.
Balungao
Balungao is a landlocked agricultural municipality in the province of Pangasinan in the Philippines, known for its hilly terrain and hot and cold springs.
- 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_69d85ccbbcd48190acb56e7cf104d8ad |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6e59a7c8190a1634a706ea68fda |
completed | April 15, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe8be6ce68819099f841d83c6ca33d |
completed | May 9, 2026, 1:20 a.m. |
| NEDg | Description generation | batch_69fe8e512adc8190a1cf47f1713a968f |
completed | May 9, 2026, 1:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe8ec3d718819098629558bc007496 |
completed | May 9, 2026, 1:32 a.m. |
Created at: April 10, 2026, 2:49 a.m.