Triple
T137752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Walter Chrysler |
E2784
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Chrysler Corporation |
E27723
|
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: Chrysler Corporation | Statement: [Walter Chrysler, employer, Chrysler Corporation]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chrysler Corporation Context triple: [Walter Chrysler, employer, Chrysler Corporation]
-
A.
Chrysler
chosen
Chrysler is a major American automobile manufacturer, historically recognized as one of the United States' "Big Three" car companies alongside General Motors and Ford.
-
B.
Dodge
Dodge is an American automotive brand best known for its performance-oriented cars, muscle vehicles, and trucks.
-
C.
General Motors
General Motors is a major American multinational automotive manufacturer known for brands such as Chevrolet, GMC, Cadillac, and Buick.
-
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.
DeSoto
DeSoto is a suburban city in the Dallas–Fort Worth metropolitan area in North Texas.
- 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_69a2521e35c08190b28e5c9f1e3c9b59 |
completed | Feb. 28, 2026, 2:25 a.m. |
| NER | Named-entity recognition | batch_69a257a6cab88190944c8f74d8d1605c |
completed | Feb. 28, 2026, 2:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3736b2ff081909a5b36a93fa16130 |
completed | Feb. 28, 2026, 10:59 p.m. |
Created at: Feb. 28, 2026, 2:31 a.m.