Triple
T35096077
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Muslim Filipinos |
E1012870
|
entity |
| Predicate | populationShareInPhilippines |
P178784
|
FINISHED |
| Object | minority |
—
|
LITERAL 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: minority | Statement: [Muslim Filipinos, populationShareInPhilippines, minority]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: populationShareInPhilippines Context triple: [Muslim Filipinos, populationShareInPhilippines, minority]
-
A.
populationShareOfCountry
chosen
Indicates the proportion of a country’s total population that is accounted for by a specified subpopulation or region.
-
B.
populationShareApproximate
Indicates that one entity’s share of a total population is approximately equal to a specified proportion or percentage, allowing for some margin of error.
-
C.
rankByAreaInPhilippines
Indicates the relative ordering of entities based on their area size specifically within the Philippines.
-
D.
populationShareInVietnam
Indicates the proportion of a larger population or group that is located in or associated with Vietnam.
-
E.
populationDistributionCountry
Indicates how a country’s population is spread or allocated across its internal regions, groups, or categories.
- F. None of above.
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_69f76dd432ec8190969bc32acfc152b1 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fd4129a8848190a5002150278ac689 |
completed | May 8, 2026, 1:49 a.m. |
| PD | Predicate disambiguation | batch_69fd3e0515ec8190937c7af71ebc3875 |
completed | May 8, 2026, 1:36 a.m. |
Created at: May 3, 2026, 4:01 p.m.