Triple
T6376216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lars Ahlfors |
E143471
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Lars |
E163910
|
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: Lars | Statement: [Lars Ahlfors, givenName, Lars]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lars Context triple: [Lars Ahlfors, givenName, Lars]
-
A.
Lars
chosen
Lars is a masculine given name of Scandinavian origin, commonly used in countries such as Norway, Sweden, and Denmark.
-
B.
Lasse
Lasse is a masculine given name of Scandinavian origin, particularly common in Finland and Sweden.
-
C.
Sven
Sven is the lovable reindeer companion in Disney's animated film "Frozen," known for his close bond with Kristoff and his expressive, dog-like personality.
-
D.
Lars Hanson
Lars Hanson was a prominent Swedish silent film actor known for his intense dramatic performances in both European cinema and early Hollywood.
-
E.
Mikael
Mikael is a masculine given name commonly used in Scandinavian and Finnish cultures, equivalent to Michael.
- 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_69c008d9f4348190ab598a2913259a1c |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0683bfc7081908b15c3c9a3c72e7b |
completed | March 22, 2026, 10:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62d9dd9dc8190b2aca25feda3e690 |
completed | March 27, 2026, 7:11 a.m. |
Created at: March 22, 2026, 4:33 p.m.