Triple
T33442872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NederlandstaligeHistorischeKaarten |
E856411
|
entity |
| Predicate | hasLanguageMetadata |
P198529
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [NederlandstaligeHistorischeKaarten, hasLanguageMetadata, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageMetadata Context triple: [NederlandstaligeHistorischeKaarten, hasLanguageMetadata, yes]
-
A.
hasLanguageType
Indicates that an entity is associated with a particular type or category of language (e.g., spoken, written, programming, sign).
-
B.
hasMetalanguage
Indicates that one entity serves as a metalanguage used to describe, define, or reason about another language or formal system.
-
C.
hasLanguageRepresentation
Indicates that an entity is expressed, encoded, or represented using a particular natural or formal language.
-
D.
hasExplicitLanguage
Indicates that the subject contains or uses language that is coarse, profane, or otherwise unsuitable for certain audiences.
-
E.
hasLanguageOn
Indicates that an entity uses or is associated with a particular language in a specific context, medium, or location.
- F. None of above. chosen
Provenance (4 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_69f34971b75881908be360bb041f003c |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fef112398081909237c3872345968b |
completed | May 9, 2026, 8:32 a.m. |
| PD | Predicate disambiguation | batch_69feefb14ec08190ab401987d8c84a23 |
completed | May 9, 2026, 8:26 a.m. |
| PDg | Predicate description generation | batch_69fef1112b0c8190a8eb027edb71e4e0 |
completed | May 9, 2026, 8:32 a.m. |
Created at: May 1, 2026, 1:37 a.m.