Triple
T7287514
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lars |
E163910
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Lasse |
E305791
|
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: Lasse | Statement: [Lars, hasVariant, Lasse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lasse Context triple: [Lars, hasVariant, Lasse]
-
A.
Lasse
chosen
Lasse is a masculine given name of Scandinavian origin, particularly common in Finland and Sweden.
-
B.
Lars
Lars is a masculine given name of Scandinavian origin, commonly used in countries such as Norway, Sweden, and Denmark.
-
C.
Niklas
Niklas is a masculine given name commonly used in Germanic and Scandinavian countries, derived from the Greek name Nikolaos.
-
D.
Rasmus
Rasmus is a masculine given name of Scandinavian origin, commonly used in countries such as Denmark, Norway, and Sweden.
-
E.
Lukas
Lukas is a masculine given name commonly used in various European countries, often associated with the biblical name Luke.
- 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_69c6886093b88190a254b1ce6db8bae7 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6eb6a73fc8190ae5ce81fd3e46d87 |
completed | March 27, 2026, 8:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7db42c8d48190a548c4242b07fb40 |
completed | March 28, 2026, 1:44 p.m. |
Created at: March 27, 2026, 2:59 p.m.