Triple
T13710888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bernard-François |
E328766
|
entity |
| Predicate | hasMorphologicalStructure |
P1250
|
FINISHED |
| Object | Bernard + hyphen + François |
—
|
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: Bernard + hyphen + François | Statement: [Bernard-François, hasMorphologicalStructure, Bernard + hyphen + François]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMorphologicalStructure Context triple: [Bernard-François, hasMorphologicalStructure, Bernard + hyphen + François]
-
A.
hasVerbalMorphology
Indicates that one linguistic element exhibits verbal inflectional properties or patterns in relation to another.
-
B.
hasHumanStructure
Indicates that one entity possesses or exhibits a structural form or organization characteristic of humans.
-
C.
hasMorphologicalType
chosen
Indicates that an entity possesses or is classified by a particular morphological type or structural form.
-
D.
hasSyllableStructure
Indicates that an entity (typically a word or morpheme) possesses a particular arrangement or pattern of syllables.
-
E.
hasNominalMorphology
Indicates that an entity possesses a system of nominal morphology, such as inflectional or derivational markers on nouns.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd43949e6c8190ae5e4fa119cde33a |
completed | April 13, 2026, 7:27 p.m. |
| PD | Predicate disambiguation | batch_69dbbe92d77c81908e0244cffb7f78c5 |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 9:54 p.m.