Triple
T2855894
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SS-Obergruppenführer |
E63198
|
entity |
| Predicate | woreInsignia |
P9510
|
FINISHED |
| Object | three oak leaves on SS collar patch |
—
|
LITERAL 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: three oak leaves on SS collar patch | Statement: [SS-Obergruppenführer, woreInsignia, three oak leaves on SS collar patch]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: woreInsignia Context triple: [SS-Obergruppenführer, woreInsignia, three oak leaves on SS collar patch]
-
A.
hasInsigniaWornBy
chosen
Indicates that a particular insignia is worn by a specified entity (such as a person, group, or organization).
-
B.
hasTypeOfInsignia
Indicates that an entity bears or is associated with a specific kind or category of insignia.
-
C.
hasMilitaryApron
Indicates that a location or facility includes a designated apron area specifically used for military aircraft operations.
-
D.
wearsOnUniform
Indicates that an item is part of and is worn as a component of a uniform.
-
E.
wearsUniformSimilarTo
Indicates that one entity wears a uniform that is similar in appearance or style to the uniform worn by another entity.
- F. None of above.
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_69ab4c41e8c08190a9e8f5249cc12610 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf62308081908a65decdd5d6f918 |
completed | March 7, 2026, 8:18 a.m. |
| PD | Predicate disambiguation | batch_69abdd10aef88190b750aae07e7df4dc |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:02 p.m.