Triple
T8463714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lena |
E200105
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Lene |
E440566
|
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: Lene | Statement: [Lena, hasVariant, Lene]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lene Context triple: [Lena, hasVariant, Lene]
-
A.
Lene
chosen
Lene is a feminine given name, commonly used in Scandinavian and German-speaking countries, often as a short form of longer names like Helene or Marlene.
-
B.
Lena Ek
Lena Ek is a Swedish Centre Party politician and former Minister for the Environment in Sweden.
-
C.
Lene Christensen
Lene Christensen is a Danish professional football goalkeeper known for playing in the Danish national team setup and in top-tier European women’s club football.
-
D.
Anette
Anette is a feminine given name, commonly used in various European countries and considered a variant of names like Annette or Annette-derived forms.
-
E.
Vibeke
Vibeke is a Scandinavian feminine given name of Old Norse origin, traditionally used in Denmark and Norway.
- 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_69ca83198c4c8190a337bf717d1813f5 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe4a39bd48190b72be7e03cff323b |
completed | March 31, 2026, 3:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce39d5f50081908e273d5286a0d397 |
completed | April 2, 2026, 9:41 a.m. |
Created at: March 30, 2026, 6:10 p.m.