Triple
T10237684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nicolas Buon |
E243507
|
entity |
| Predicate | roleInBookTrade |
P82842
|
FINISHED |
| Object | Parisian printer-bookseller |
—
|
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: Parisian printer-bookseller | Statement: [Nicolas Buon, roleInBookTrade, Parisian printer-bookseller]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInBookTrade Context triple: [Nicolas Buon, roleInBookTrade, Parisian printer-bookseller]
-
A.
relationshipToBooks
Indicates the nature or type of connection an entity has with one or more books, such as ownership, authorship, usage, or preference.
-
B.
roleInAcquisition
Indicates that an entity holds a specific role or function within an acquisition event or transaction.
-
C.
bookWriter
Indicates that a person is the author who wrote the specified book.
-
D.
bookDivision
Indicates that one entity is a distinct section, part, or subdivision within a larger book.
-
E.
majorTradeRole
chosen
Indicates that an entity plays a primary or highly significant role in trade activities or commercial exchange within a given 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_69d381b0f97c819085c9b45799a5fb7c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d23b620c8190b8a72d0eb0d16b93 |
completed | April 7, 2026, 9:45 a.m. |
| PD | Predicate disambiguation | batch_69d4d1e9798c8190b437d53d48554ba1 |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:23 a.m.