Triple
T153223
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grand Cross of the Order of Merit of the Federal Republic of Germany |
E3475
|
entity |
| Predicate | includesInsignia |
P1617
|
FINISHED |
| Object | badge worn on a sash |
—
|
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: badge worn on a sash | Statement: [Grand Cross of the Order of Merit of the Federal Republic of Germany, includesInsignia, badge worn on a sash]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesInsignia Context triple: [Grand Cross of the Order of Merit of the Federal Republic of Germany, includesInsignia, badge worn on a sash]
-
A.
hasTypeOfInsignia
chosen
Indicates that an entity bears or is associated with a specific kind or category of insignia.
-
B.
includes
Indicates that one entity contains, encompasses, or has another entity as a part, member, or subset.
-
C.
hasLogoText
Indicates that an entity’s logo includes specific textual content or wording.
-
D.
formerBrand
Indicates that an entity was previously used or recognized as a brand for another entity but is no longer its current brand.
-
E.
includesBowl
Indicates that something contains or has a bowl as one of its components or elements.
- 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_69a252868de4819080e21c9938bfe8b6 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a258e0b11c8190b7b5cf3c354c47ce |
completed | Feb. 28, 2026, 2:54 a.m. |
| PD | Predicate disambiguation | batch_69a2565c727c8190bca9ba6ca52f216a |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.