Triple
T11981465
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mistress Ford |
E285169
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Ford |
E65451
|
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: Ford | Statement: [Mistress Ford, spouse, Ford]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ford Context triple: [Mistress Ford, spouse, Ford]
-
A.
Ford
Ford is a town in the Metropolitan Borough of Sefton, Merseyside, England, forming part of the northern suburbs of Liverpool.
-
B.
Ford
chosen
Ford is a common English surname borne by numerous notable individuals, including U.S. President Gerald Ford.
-
C.
Ford
Ford is a small village in the Arun District of West Sussex, England, known for its rural character and nearby railway station.
-
D.
Ford Motor Company
Ford Motor Company is a major American automobile manufacturer, founded by Henry Ford, known for pioneering assembly-line mass production and producing iconic vehicles like the Model T and F-Series trucks.
-
E.
General Motors
General Motors is a major American multinational automotive manufacturer known for brands such as Chevrolet, GMC, Cadillac, and Buick.
- 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_69d6ab44a77c8190a652f4b27164e4ef |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90395a8788190bfbb3506c29e3825 |
completed | April 10, 2026, 2:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f4721913108190bd767c671f6484de |
completed | May 1, 2026, 9:27 a.m. |
Created at: April 8, 2026, 9:46 p.m.