Triple
T5990243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bishopric of Vlissingen |
E133329
|
entity |
| Predicate | languageOfDailyLife |
P42338
|
FINISHED |
| Object | Middle Dutch |
—
|
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: Middle Dutch | Statement: [Bishopric of Vlissingen, languageOfDailyLife, Middle Dutch]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfDailyLife Context triple: [Bishopric of Vlissingen, languageOfDailyLife, Middle Dutch]
-
A.
isWidelySpokenIn
Indicates that a language is spoken by a large portion of the population across many regions or communities within a specified area.
-
B.
typicalLanguages
chosen
Indicates the languages that are commonly or characteristically used, spoken, or associated with a given entity.
-
C.
languageOfExpression
Indicates that a particular language is used as the medium or form in which an expression (such as a text, utterance, or work) is realized.
-
D.
deFactoLanguage
Indicates that a language is used in practice as the primary or common language in a context, even if it has no official legal status there.
-
E.
languageDiversity
Indicates the degree to which multiple distinct languages are present and used within a given context or population.
- 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_69c0087010d081908bb8142342d63330 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04dc8ab648190beb1bc141796894e |
completed | March 22, 2026, 8:15 p.m. |
| PD | Predicate disambiguation | batch_69c049e152e88190979ab80cb9b50321 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:05 p.m.