Triple
T37659031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Distagon lens design |
E937671
|
entity |
| Predicate | hasBrandNameUse |
P151324
|
FINISHED |
| Object | Zeiss Distagon |
—
|
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: Zeiss Distagon | Statement: [Distagon lens design, hasBrandNameUse, Zeiss Distagon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBrandNameUse Context triple: [Distagon lens design, hasBrandNameUse, Zeiss Distagon]
-
A.
hasBrandName
Indicates that an entity is associated with or identified by a specific brand name.
-
B.
brandNameUsedIn
chosen
Indicates that a particular brand name is used or appears within a specified context, such as a product, document, or communication.
-
C.
usedBrand
Indicates that an entity has utilized, applied, or operated a particular brand in some context.
-
D.
usesBrandCharacter
Indicates that one entity employs or features another entity’s brand character (such as a mascot or branded persona) in its materials, products, or communications.
-
E.
usedBrandOf
Indicates that one entity made use of or operated an item, product, or service associated with a particular brand.
- 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_69f76ed6df7c8190b018e5baea716ceb |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcd867f36081908c88c55a6a1404c1 |
completed | May 7, 2026, 6:22 p.m. |
| PD | Predicate disambiguation | batch_69fcd1f47b188190b4cf4b4c748d9d03 |
completed | May 7, 2026, 5:55 p.m. |
Created at: May 3, 2026, 4:18 p.m.