Triple
T21650121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gertrude Seiberling |
E534314
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Seiberling |
—
|
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: Seiberling | Statement: [Gertrude Seiberling, familyName, Seiberling]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seiberling Context triple: [Gertrude Seiberling, familyName, Seiberling]
-
A.
Seiberling
chosen
Seiberling is a surname most notably associated with American industrialist Frank Seiberling, co-founder of the Goodyear Tire & Rubber Company.
-
B.
Trumbauer
Trumbauer is a surname most notably associated with American architect Horace Trumbauer, known for his grand Gilded Age mansions and institutional buildings.
-
C.
Schnitzer
Schnitzer is a German-language surname borne by various individuals and families of German or Central European origin.
-
D.
Kleiser
Kleiser is a surname most notably associated with American film director Randal Kleiser, known for directing the musical romantic comedy "Grease."
-
E.
Stottlemeyer
Stottlemeyer is the surname of Captain Leland Stottlemeyer, a central police character from the television series "Monk."
- 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_69e0c466aec88190ba39c7543dbc8ba2 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef5913cd9c81908a6ce9bc741416bf |
completed | April 27, 2026, 12:39 p.m. |
Created at: April 16, 2026, 6:35 p.m.