Triple
T4671326
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2nd Army |
E102968
|
entity |
| Predicate | notableCommander |
P1197
|
FINISHED |
| Object | Walter Weiß |
E340908
|
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: Walter Weiß | Statement: [2nd Army, notableCommander, Walter Weiß]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Walter Weiß Context triple: [2nd Army, notableCommander, Walter Weiß]
-
A.
Walter Weiss
chosen
Walter Weiss is a relatively obscure individual whose specific public achievements or biographical details are not widely documented.
-
B.
Mark Weissenstern
Mark Weissenstern is an electronics industry figure best known as a founder of the semiconductor company Signetics.
-
C.
Kurt Weiss
Kurt Weiss is an individual notable enough to be recognized as a namesake of the surname Weiss, though specific widely known biographical details are not clearly established.
-
D.
Harry Dreyfuss
Harry Dreyfuss is an American writer and actor known for his essays, political commentary, and for speaking publicly about his experiences with harassment in the entertainment industry.
-
E.
Winston White
Winston White is a variant of the Winston brand of cigarettes distinguished primarily by its white packaging and milder blend.
- 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_69bd43d9cba4819086c1ab1c2d9d2133 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd63506090819083ff8271adc5ef75 |
completed | March 20, 2026, 3:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be039538048190b4075daf47355cee |
completed | March 21, 2026, 2:33 a.m. |
Created at: March 20, 2026, 1:15 p.m.