Triple
T2353362
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mulally |
E47497
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Alan Mulally |
E8612
|
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: Alan Mulally | Statement: [Mulally, hasNotableBearer, Alan Mulally]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alan Mulally Context triple: [Mulally, hasNotableBearer, Alan Mulally]
-
A.
Alan Mulally
chosen
Alan Mulally is an American engineer and business executive best known for leading Ford Motor Company’s turnaround as its CEO during the late 2000s financial crisis.
-
B.
Jim Farley
Jim Farley is an American business executive who serves as the president and chief executive officer of Ford Motor Company.
-
C.
Jeff Immelt
Jeff Immelt is an American business executive best known for serving as the CEO and chairman of General Electric from 2001 to 2017.
-
D.
Jack Welch
Jack Welch was a prominent American business executive best known for his transformative and often controversial tenure as CEO of General Electric from 1981 to 2001.
-
E.
Lee Iacocca
Lee Iacocca was a prominent American automobile executive best known for his leadership at Ford and Chrysler and his pivotal role in shaping the modern U.S. car industry.
- 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_69a88a1b678c8190bce986922ba60ce0 |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abc6fa6ecc8190821c9d5db341cf19 |
completed | March 7, 2026, 6:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae9633ce3c81908581e7e0f8211ac1 |
completed | March 9, 2026, 9:43 a.m. |
Created at: March 4, 2026, 7:54 p.m.