Triple
T19858179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John |
E477188
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Jean |
—
|
NE NERFINISHED |
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: Jean | Statement: [John, hasVariant, Jean]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jean Context triple: [John, hasVariant, Jean]
-
A.
Jean
Jean is the given first name of Henry Dunant, the Swiss humanitarian who founded the Red Cross and received the first Nobel Peace Prize.
-
B.
Jean
chosen
Jean is a common French given name used for both males and females, equivalent to "John" in English.
-
C.
Jean
Jean is a fictional mother character from the film "Sweet Sixteen."
-
D.
Jean
Jean is a character in the Nigerian comedy film "The Wedding Party," which follows the chaos and drama surrounding a high-profile Lagos wedding.
-
E.
Jean
Jean is the given first name of the Canadian novelist Margaret Laurence, a central figure in 20th-century Canadian literature.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51e7d948190aedbcd6c30361c39 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6586dbbf0819089e7157d416aeaaf |
completed | April 20, 2026, 4:46 p.m. |
Created at: April 10, 2026, 1:51 p.m.