Triple
T13983931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | International Harvester |
E336383
|
entity |
| Predicate | hadSubsidiary |
P9212
|
FINISHED |
| Object | Cub Cadet |
E1074005
|
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: Cub Cadet | Statement: [International Harvester, hadSubsidiary, Cub Cadet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cub Cadet Context triple: [International Harvester, hadSubsidiary, Cub Cadet]
-
A.
Cub Cadet
chosen
Cub Cadet is an American manufacturer best known for its lawn and garden tractors, mowers, and outdoor power equipment.
-
B.
Husqvarna Garden
Husqvarna Garden is a multi-purpose indoor arena in Jönköping, Sweden, best known as the home venue of the professional ice hockey team HV71.
-
C.
Stihl
Stihl is a German manufacturer renowned worldwide for its chainsaws and outdoor power equipment.
-
D.
Doosan Bobcat
Doosan Bobcat is a global construction equipment manufacturer best known for producing compact machinery such as skid-steer loaders, excavators, and utility vehicles.
-
E.
John Deere
John Deere is a leading American manufacturer of agricultural, construction, and forestry machinery, best known for its green and yellow farm equipment.
- 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_69d81c639e808190a0e4b4f3d31c6a59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2ea2e8808190a1203a6386224bd8 |
completed | April 14, 2026, 12:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbc32593e08190a1fe8466705c7fe8 |
completed | May 6, 2026, 10:39 p.m. |
Created at: April 9, 2026, 10:18 p.m.