Triple
T13590519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Max Born Award |
E324680
|
entity |
| Predicate | presentedBy |
P83
|
FINISHED |
| Object | Optica |
E4713
|
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: Optica | Statement: [Max Born Award, presentedBy, Optica]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Optica Context triple: [Max Born Award, presentedBy, Optica]
-
A.
Optica
chosen
Optica is a leading scientific society dedicated to advancing the study and application of optics and photonics worldwide.
-
B.
American Optical Company
American Optical Company is a historic U.S. manufacturer best known for producing eyeglasses, optical instruments, and pioneering lens technologies.
-
C.
Opti
Opti is a friendly, futuristic robot character that served as one of the official mascots of Expo 2020 Dubai.
-
D.
ZEISS
ZEISS is a renowned German optics company best known for its high-quality lenses and imaging technologies used in cameras, microscopes, and industrial systems.
-
E.
Dioptrique
Dioptrique is a scientific treatise by René Descartes that lays out his pioneering theories on light and optics, including the law of refraction.
- 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_69d80769eaf081909d82f44e484d6113 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb055cc98819091fab597b69e5e3e |
completed | April 12, 2026, 2:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f76bc347b881908267455f3bdd50e8 |
completed | May 3, 2026, 3:37 p.m. |
Created at: April 9, 2026, 9:49 p.m.