Triple
T36618264
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Officer (Pinuno) of the Order of Lakandula |
E903663
|
entity |
| Predicate | rankTitleInFilipino |
P34150
|
FINISHED |
| Object | Pinuno |
—
|
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: Pinuno | Statement: [Officer (Pinuno) of the Order of Lakandula, rankTitleInFilipino, Pinuno]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankTitleInFilipino Context triple: [Officer (Pinuno) of the Order of Lakandula, rankTitleInFilipino, Pinuno]
-
A.
lengthRankingInPhilippines
Indicates the relative position of something in an ordered list based on its length specifically within the context of the Philippines.
-
B.
officeHolderTitleInFilipino
chosen
Indicates the official title or designation of an office holder expressed in the Filipino language.
-
C.
rankInPhilippineHonorsSystem
Indicates the specific level or position an entity holds within the official hierarchy of the Philippine honors and awards system.
-
D.
useRankTitles
Indicates that rank-based titles are applied or displayed for the entities involved.
-
E.
rankInSpanishHonoursSystem
Indicates the specific hierarchical position a person holds within the system of Spanish honors and decorations.
- 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_69f76e6960e4819092047756ceb9a17e |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c777e924819081a6634f549fe552 |
completed | May 3, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69f7c477a4d481908f52e55b6688f60c |
completed | May 3, 2026, 9:56 p.m. |
Created at: May 3, 2026, 4:11 p.m.