Triple
T11460488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jen |
E271644
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Jenni |
E257219
|
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: Jenni | Statement: [Jen, relatedName, Jenni]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jenni Context triple: [Jen, relatedName, Jenni]
-
A.
Jenni
chosen
Jenni is a feminine given name commonly used as a diminutive or variant of Jennifer.
-
B.
Jenni Fortune
Jenni Fortune is a central fictional character in Sebastian Faulks's contemporary novel "A Week in December," around whom part of the book’s interwoven modern London narrative revolves.
-
C.
Jenni Murray
Jenni Murray is a British journalist and broadcaster best known for her long tenure as the presenter of BBC Radio 4’s Woman’s Hour.
-
D.
Ruby Wax
Ruby Wax is an American-British comedian, actress, and mental health campaigner known for her sharp, satirical interviewing style on British television.
-
E.
Miranda Hart
Miranda Hart is an English comedian, actress, and writer best known for her self-titled sitcom "Miranda" and her distinctive physical and observational comedy.
- 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_69d6aadff8888190a13f253f0d460874 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d822f384f08190b1150ed1389dd31a |
completed | April 9, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5e91f1bb881909a9c36d837e4059b |
completed | April 20, 2026, 8:51 a.m. |
Created at: April 8, 2026, 9:35 p.m.