Triple
T4989225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gunnar Asplund |
E112087
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Gunnar |
E30461
|
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: Gunnar | Statement: [Gunnar Asplund, givenName, Gunnar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gunnar Context triple: [Gunnar Asplund, givenName, Gunnar]
-
A.
Gunnar
chosen
Gunnar is a masculine given name of Old Norse origin, commonly used in Scandinavian countries and associated with warriors or bold fighters.
-
B.
Ivar
Ivar is a masculine given name of Old Norse origin, traditionally used in Scandinavian countries.
-
C.
Ragnar
Ragnar is a masculine given name of Old Norse origin, historically associated with Viking-age Scandinavia and later borne by various notable figures.
-
D.
Grettir Ásmundarson
Grettir Ásmundarson is a legendary Icelandic outlaw and warrior famed for his superhuman strength, tragic fate, and central role in the medieval saga bearing his name.
-
E.
Gunnar Cauthery
Gunnar Cauthery is an actor best known for his role in the television series "Mars."
- 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_69bd441be7bc8190b530362d427b97d2 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd727eae1c819085e5548faadbd162 |
completed | March 20, 2026, 4:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be8a2496a48190a517c92b85834db9 |
completed | March 21, 2026, 12:08 p.m. |
Created at: March 20, 2026, 1:34 p.m.