Triple
T11940959
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jocelyn |
E284174
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Joselyn |
E284174
|
NE 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: Joselyn | Statement: [Jocelyn, hasVariant, Joselyn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joselyn Context triple: [Jocelyn, hasVariant, Joselyn]
-
A.
Sheyla
Sheyla is a feminine given name, typically considered a variant of Sheila or Shayla and used in various cultures.
-
B.
Jocelyn
chosen
Jocelyn is a given name commonly used for people of any gender, often associated with the nickname "Jo."
-
C.
Marlen
Marlen is a village district of the town of Kehl in the German state of Baden-Württemberg.
-
D.
Mikaela
Mikaela is a feminine given name most prominently associated with American alpine ski champion Mikaela Shiffrin.
-
E.
Everly Carganilla
Everly Carganilla is a young American child actress known for roles in film and television, including the Netflix series "The Chair."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90342bb908190a019ac91a2b82f3d |
completed | April 10, 2026, 2:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f471ba7fd88190909596e6e01e8714 |
completed | May 1, 2026, 9:26 a.m. |
Created at: April 8, 2026, 9:45 p.m.