Triple
T2150128
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Tyndale |
E47159
|
entity |
| Predicate | translatedIntoLanguage |
P21151
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [William Tyndale, translatedIntoLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: translatedIntoLanguage Context triple: [William Tyndale, translatedIntoLanguage, English]
-
A.
translationTargetLanguage
chosen
Indicates the language into which content is being or has been translated.
-
B.
otherLanguage
Indicates that an entity has or uses an additional language distinct from its primary or main language.
-
C.
translationDirection
Indicates the source and target languages involved in a translation, specifying the direction from the original language to the translated language.
-
D.
languageEquivalent
Indicates that two linguistic expressions convey the same meaning or function across different languages or language varieties.
-
E.
translationMethod
Indicates the technique or process used to translate content from one language or form to another.
- 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_69a88a1933e0819094f18426ed74180f |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abbe45dbf481909c0da056a1a99ece |
completed | March 7, 2026, 5:57 a.m. |
| PD | Predicate disambiguation | batch_69abbd9a60648190b20b116be5c7ad98 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:44 p.m.