Triple
T22940016
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Glendinning |
E569697
|
entity |
| Predicate | isCharacterInLanguage |
P130300
|
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: [Mary Glendinning, isCharacterInLanguage, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isCharacterInLanguage Context triple: [Mary Glendinning, isCharacterInLanguage, English]
-
A.
isCharacterInWorkLanguage
Indicates that a character appears in a work (e.g., book, film, game) in a specific language version or localization.
-
B.
hasLanguageCharacter
Indicates that an entity uses, contains, or is associated with a specific written or symbolic character from a language.
-
C.
isLanguageOf
Indicates that a particular language is used as the official or primary language associated with a given entity (such as a person, document, or region).
-
D.
RLanguageIs
chosen
Indicates that one entity is the language associated with, used by, or expressed in relation to another entity.
-
E.
isLinguaFrancaOf
Indicates that a language serves as a common medium of communication between speakers of different native languages within a particular region, community, or context.
- 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_69e24590862c8190858f180ad302adab |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1813844b88190b05d3829b0c423c4 |
completed | April 29, 2026, 3:55 a.m. |
| PD | Predicate disambiguation | batch_69ef3b882e708190b0eb0c87021c75b8 |
completed | April 27, 2026, 10:33 a.m. |
Created at: April 17, 2026, 3:45 p.m.