Triple
T9498228
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Presidentti |
E229067
|
entity |
| Predicate | EnglishEquivalent |
P31361
|
FINISHED |
| Object | President |
—
|
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: President | Statement: [Presidentti, EnglishEquivalent, President]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: EnglishEquivalent Context triple: [Presidentti, EnglishEquivalent, President]
-
A.
EnglishTranslation
chosen
Indicates that one expression is the English-language translation equivalent of another expression.
-
B.
languageEquivalent
Indicates that two linguistic expressions convey the same meaning or function across different languages or language varieties.
-
C.
EnglishObjective
Indicates that an entity has English language proficiency or achievement as a goal or target to be attained.
-
D.
equivalentIn
Indicates that two entities are considered logically or functionally the same in meaning, status, or effect within a given context.
-
E.
equivalentTitleInEngland
Indicates that one title corresponds to an equivalent or matching title within the context of England’s system of titles.
- 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_69ca84753660819098e8d416e89e26ae |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd95ef06b88190b7a840caddea3e38 |
completed | April 1, 2026, 10:02 p.m. |
| PD | Predicate disambiguation | batch_69cca5651a588190a3cfebe249a223e5 |
completed | April 1, 2026, 4:56 a.m. |
Created at: March 30, 2026, 7:56 p.m.