Triple
T5046031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United Kingdom and France |
E113665
|
entity |
| Predicate | shareLanguageInfluence |
P61006
|
FINISHED |
| Object | French loanwords in 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: French loanwords in English | Statement: [United Kingdom and France, shareLanguageInfluence, French loanwords in English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: shareLanguageInfluence Context triple: [United Kingdom and France, shareLanguageInfluence, French loanwords in English]
-
A.
languageInfluence
Indicates that one language has an effect on the development, usage, or characteristics of another language.
-
B.
influencedLanguage
Indicates that one language has had an effect on the development, structure, or usage of another language.
-
C.
influencedLanguageFamily
Indicates that one language family has had a significant impact on the development, structure, or usage of another language family.
-
D.
sharesLanguageWith
Indicates that two entities use at least one common language for communication.
-
E.
historicalLanguageInfluenceOn
Indicates that one language has had a shaping or contributory effect on the development, vocabulary, structure, or usage of another language over time.
- 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_69bd44391fc48190a311ce9c826c209b |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73fe99688190961708f5d8eb6bff |
completed | March 20, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69bd71529d608190a53470ba6c14bb1d |
completed | March 20, 2026, 4:09 p.m. |
| PDg | Predicate description generation | batch_69bd73617f348190b2fa68a0ef4fc7b1 |
completed | March 20, 2026, 4:18 p.m. |
Created at: March 20, 2026, 1:37 p.m.