Triple
T13961517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kristen |
E335804
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Kristin |
E608731
|
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: Kristin | Statement: [Kristen, hasVariant, Kristin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kristin Context triple: [Kristen, hasVariant, Kristin]
-
A.
Kristin
chosen
Kristin is a feminine given name commonly used in English-speaking and Scandinavian countries, often considered a variant of Christine or Christina.
-
B.
Kristin
Kristin is one of the official mascots of the 1994 Winter Olympics held in Lillehammer, Norway.
-
C.
Kristin
Kristin is the given name of the acclaimed British-French actress Kristin Scott Thomas, known for her roles in films such as "The English Patient" and "Four Weddings and a Funeral."
-
D.
Kristin
Kristin is the central female protagonist of Sigrid Undset’s historical novel trilogy "Kristin Lavransdatter," set in medieval Norway.
-
E.
Kristen
Kristen is a feminine given name commonly used in English-speaking countries, often associated with notable figures in entertainment and public life.
- 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e7c73d48190b8e02971b5a8ed5f |
completed | April 14, 2026, 12:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fba1d6b2e88190b4d2237413f509ee |
completed | May 6, 2026, 8:17 p.m. |
Created at: April 9, 2026, 10:18 p.m.