Triple
T19202315
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MacGraw |
E480135
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | McGraw |
—
|
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: McGraw | Statement: [MacGraw, hasVariant, McGraw]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: McGraw Context triple: [MacGraw, hasVariant, McGraw]
-
A.
McGraw
chosen
McGraw is a surname most famously associated with John McGraw, the legendary early 20th-century Major League Baseball manager of the New York Giants.
-
B.
Magruder
Magruder is a surname most notably associated with Jeb Stuart Magruder, a key figure in the Watergate scandal during the Nixon administration.
-
C.
McClurg
McClurg is the namesake of the historic McClurg Building, a notable structure recognized for its architectural and cultural significance.
-
D.
McGillis
McGillis is a surname most notably associated with American actress Kelly McGillis, known for her roles in films such as "Top Gun" and "Witness."
-
E.
Holt
Holt is a historic American brand best known for its early crawler tractors and as a predecessor to the Caterpillar brand in the heavy machinery industry.
- 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_69d8e8cb8c348190b52075823911c869 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5f9991ba48190ab92805daec3877b |
completed | April 20, 2026, 10:02 a.m. |
Created at: April 10, 2026, 1:12 p.m.