Triple
T15974595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bonnie |
E387411
|
entity |
| Predicate | etymologyUltimatelyFrom |
P5801
|
FINISHED |
| Object | French word "bon" |
—
|
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 word "bon" | Statement: [Bonnie, etymologyUltimatelyFrom, French word "bon"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: etymologyUltimatelyFrom Context triple: [Bonnie, etymologyUltimatelyFrom, French word "bon"]
-
A.
etymologyRelatesTo
Indicates a relationship where one term’s origin, history, or derivation is connected to another linguistic form, word, or source.
-
B.
etymologyStatus
Indicates the status or reliability classification of an etymological explanation for a term or name.
-
C.
etymologyType
Indicates the specific kind or category of etymological relationship that links a term to its linguistic origin or source.
-
D.
etymology
Indicates the historical origin and development of a word or term, including its source language and form.
-
E.
etymologicalSource
chosen
Indicates that one term or name originates from, is derived from, or has its roots in another term or name.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142d6fb588190b4176eab4bbae774 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:54 a.m.