Triple
T13574300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Assar Gabrielsson |
E324241
|
entity |
| Predicate | hasEmployer |
P7
|
FINISHED |
| Object | SKF |
E681815
|
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: SKF | Statement: [Assar Gabrielsson, hasEmployer, SKF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SKF Context triple: [Assar Gabrielsson, hasEmployer, SKF]
-
A.
SKF
chosen
SKF is a Swedish multinational engineering company best known as one of the world’s leading manufacturers of bearings and related industrial technologies.
-
B.
FAG
FAG is a German handball club based in Göppingen, competing in national and international leagues.
-
C.
FAG
FAG is the acronym for the Guatemalan Air Force, the aerial warfare branch of Guatemala’s military responsible for air defense and support operations.
-
D.
Atlas Copco
Atlas Copco is a Swedish multinational industrial company known for manufacturing compressors, vacuum solutions, generators, pumps, power tools, and assembly systems.
-
E.
Continental Motors
Continental Motors is an American manufacturer best known for producing aircraft and military vehicle engines, including powerplants for tanks and other armored vehicles.
- 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_69d80769100c819099111274614f5ed2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb02b1f108190a12af382d1de70bb |
completed | April 12, 2026, 2:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f76bba21f88190b8952fb0879e623d |
completed | May 3, 2026, 3:37 p.m. |
Created at: April 9, 2026, 9:48 p.m.