Triple
T21975336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Province of Bulacan |
E542689
|
entity |
| Predicate | hasDemonym |
P191
|
FINISHED |
| Object | Bulakeño |
—
|
NE NERFINISHED |
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: Bulakeño | Statement: [Province of Bulacan, hasDemonym, Bulakeño]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bulakeño Context triple: [Province of Bulacan, hasDemonym, Bulakeño]
-
A.
Bulakeño
chosen
Bulakeño refers to a person from the province of Bulacan in the Philippines, known for a rich cultural heritage, historical significance, and vibrant local traditions.
-
B.
Balagteño
A Balagteño is a resident or native of the municipality of Balagtas in the province of Bulacan, Philippines.
-
C.
Bacoleña
Bacoleña is the Spanish-derived demonym referring to a female resident or native of Bacolor, a municipality in the Philippines.
-
D.
Balangeño
A Balangeño is a resident or native of Balanga City in the province of Bataan, Philippines.
-
E.
Bangu
Bangu is a working-class neighborhood in the West Zone of Rio de Janeiro, Brazil, known for its hot climate, historic textile industry, and the Bangu Atlético Clube football team.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c48070988190909db97667b9a0ac |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f12487a1a88190abb8a51fcd533b6a |
completed | April 28, 2026, 9:20 p.m. |
Created at: April 16, 2026, 8:03 p.m.