Triple
T22066053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Ford |
E545272
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Ford |
—
|
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 | Statement: [Robert Ford, familyName, Ford]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ford Context triple: [Robert Ford, familyName, Ford]
-
A.
Ford
chosen
Ford is a common English surname borne by numerous notable individuals, including U.S. President Gerald Ford.
-
B.
Ford
Ford is a town in the Metropolitan Borough of Sefton, Merseyside, England, forming part of the northern suburbs of Liverpool.
-
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
Ford is the NATO reporting name for the Douglas F4D Skyray, a Cold War-era U.S. Navy carrier-based jet fighter.
-
E.
Ford
Ford is a major American automobile manufacturer known for pioneering assembly-line mass production and producing iconic vehicles such as the Model T and the F-Series trucks.
- 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_69e11e344dfc81909b1d88a7221329c7 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f12883d2108190a6127783f8f635fc |
completed | April 28, 2026, 9:37 p.m. |
Created at: April 16, 2026, 8:27 p.m.