Triple
T9084886
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sir John Kingman |
E217725
|
entity |
| Predicate | hasHonor |
P11
|
FINISHED |
| Object | FRS |
E6383
|
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: FRS | Statement: [Sir John Kingman, hasHonor, FRS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FRS Context triple: [Sir John Kingman, hasHonor, FRS]
-
A.
FRS
chosen
FRS is the post-nominal title used by Fellows of the Royal Society, denoting distinguished scientists elected to the United Kingdom’s national academy of sciences.
-
B.
FRF
FRF is a NUTS 1 statistical region code designating the French region of Brittany within the European Union’s territorial classification system.
-
C.
FRAS
FRAS is a professional post-nominal title indicating fellowship in the Royal Astronomical Society, typically awarded to individuals who have made significant contributions to astronomy or geophysics.
-
D.
FRSAD
FRSAD (Functional Requirements for Subject Authority Data) is an IFLA conceptual model that defines how subject authority data should be structured and related to support effective subject access in library and information systems.
-
E.
FSR
FSR is AMD's open-source spatial upscaling technology designed to boost gaming performance and image quality across a wide range of GPUs.
- 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_69ca83d7a0388190ba1af89ed7ba36f9 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc960d0b008190b0e9e61fac45101d |
completed | April 1, 2026, 3:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cffe38f0048190a9fb15e73d9bcd50 |
completed | April 3, 2026, 5:51 p.m. |
Created at: March 30, 2026, 7:13 p.m.