Triple
T13319659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morong, Bataan |
E317281
|
entity |
| Predicate | hasBarangay |
P29835
|
FINISHED |
| Object | Mabayo |
E1033622
|
NE FINISHED |
How this triple was built (2 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: Mabayo | Statement: [Morong, Bataan, hasBarangay, Mabayo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mabayo Context triple: [Morong, Bataan, hasBarangay, Mabayo]
-
A.
Mabayo
chosen
Mabayo is a coastal barangay in the municipality of Morong in the province of Bataan, Philippines.
-
B.
Matabaan
Matabaan is a town in central Somalia that serves as one of the urban centers within the federal member state of Hirshabelle.
-
C.
Maasara
Maasara is an industrial and residential district in the Greater Cairo area of Egypt, known for its factories and proximity to the Nile.
-
D.
Mbyá
Mbyá are an Indigenous Guaraní-speaking people of South America, primarily living in regions of Paraguay, Brazil, and Argentina, with a distinct language, culture, and spiritual tradition.
-
E.
Kabaena
Kabaena is an island in Indonesia known for its location off the coast of Sulawesi and its mix of coastal and hilly landscapes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d806b4d62c81908d4ced1665414be5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d990faa95481908a7fd297959c062e |
completed | April 11, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f71f2a8ba88190a59bc4840ec8ad13 |
completed | May 3, 2026, 10:10 a.m. |
Created at: April 9, 2026, 9:29 p.m.