Triple
T3307718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Garter motto "Honi soit qui mal y pense" |
E69489
|
entity |
| Predicate | translationVariant |
P34737
|
FINISHED |
| Object | Evil be to him who evil thinks |
—
|
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: Evil be to him who evil thinks | Statement: [Garter motto "Honi soit qui mal y pense", translationVariant, Evil be to him who evil thinks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: translationVariant Context triple: [Garter motto "Honi soit qui mal y pense", translationVariant, Evil be to him who evil thinks]
-
A.
languageVariant
Indicates that one language is a variant, dialect, or localized form of another language.
-
B.
linguisticVariant
chosen
Indicates that one linguistic form is an alternative version or expression of another within the same or closely related language context.
-
C.
translationMethod
Indicates the technique or process used to translate content from one language or form to another.
-
D.
translationTargetLanguage
Indicates the language into which content is being or has been translated.
-
E.
translationDirection
Indicates the source and target languages involved in a translation, specifying the direction from the original language to the translated language.
- 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_69ad859f218081909458d2cebbf57565 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb0cc15088190b51c311c6590df04 |
completed | March 8, 2026, 5:24 p.m. |
| PD | Predicate disambiguation | batch_69ada4282730819092aa39c5f9269df0 |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:11 p.m.