Triple
T18896532
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ford Taunus 15M |
E462222
|
entity |
| Predicate | category |
P87
|
FINISHED |
| Object | Ford vehicles |
—
|
NE NERFINISHED |
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: Ford vehicles | Statement: [Ford Taunus 15M, category, Ford vehicles]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ford vehicles Context triple: [Ford Taunus 15M, category, Ford vehicles]
-
A.
Ford vehicles
chosen
Ford vehicles are a range of automobiles produced by the Ford Motor Company, including cars, trucks, SUVs, and commercial vehicles sold worldwide.
-
B.
Toyota vehicles
Toyota vehicles are a globally recognized line of reliable, fuel-efficient cars, trucks, and SUVs produced by the Japanese automaker Toyota.
-
C.
Ford Models
Ford Models is a prominent international modeling agency known for representing high-profile fashion models and shaping the modern modeling industry.
-
D.
Ford Ranges
Ford Ranges are a group of largely ice-covered mountain ranges in western Antarctica’s Marie Byrd Land, notable for their remote, rugged peaks and extensive glaciation.
-
E.
Ford platforms
Ford platforms are the underlying vehicle architectures developed by the Ford Motor Company to support multiple models sharing common structural and mechanical components.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dcfd05bc819088903cca13cc2846 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c47f6c948190918ade08bb88f1fd |
completed | April 20, 2026, 6:15 a.m. |
Created at: April 10, 2026, 11:58 a.m.