Triple
T9123289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schroder |
E218908
|
entity |
| Predicate | hasSpellingVariant |
P457
|
FINISHED |
| Object | Schroeter |
E779628
|
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: Schroeter | Statement: [Schroder, hasSpellingVariant, Schroeter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Schroeter Context triple: [Schroder, hasSpellingVariant, Schroeter]
-
A.
Schroeter
chosen
Schroeter is a German-language surname of likely occupational or locational origin, borne by various notable figures in fields such as music, science, and the arts.
-
B.
Wolterstorff
Wolterstorff is the surname of Nicholas Wolterstorff, a prominent American philosopher known for his work in epistemology, aesthetics, and the philosophy of religion.
-
C.
Hufstedler
Hufstedler is the surname of Shirley Hufstedler, a prominent American judge and the first U.S. Secretary of Education.
-
D.
Stahlecker
Stahlecker is a German-language surname most notably associated with Franz Walter Stahlecker, a high-ranking SS officer and Nazi official during World War II.
-
E.
Koldewey
Koldewey is a German surname most notably associated with Robert Koldewey, the archaeologist who led the excavation of ancient Babylon.
- 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_69ca83dddd548190983b96c664f7f367 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8b5fa188190be6465e74cf26915 |
completed | April 1, 2026, 5:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d047c55a988190bf2dd63a0d0a2743 |
completed | April 3, 2026, 11:05 p.m. |
Created at: March 30, 2026, 7:17 p.m.