Triple
T8691925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chernsky Uyezd |
E206311
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Chern |
E750410
|
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: Chern | Statement: [Chernsky Uyezd, capital, Chern]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chern Context triple: [Chernsky Uyezd, capital, Chern]
-
A.
Chern
chosen
Chern is a settlement in Russia that historically served as the administrative center of Chernsky Uyezd.
-
B.
Chern character
The Chern character is a fundamental homomorphism from K-theory to cohomology that translates vector bundles into characteristic classes, playing a central role in index theory and algebraic topology.
-
C.
Chern classes
Chern classes are fundamental topological invariants in differential and algebraic geometry that classify complex vector bundles and capture their curvature and twisting properties.
-
D.
Chern–Weil theory
Chern–Weil theory is a framework in differential geometry that constructs characteristic classes of vector bundles from curvature forms, linking topology and geometry through invariant polynomials.
-
E.
Pontryagin classes
Pontryagin classes are characteristic classes associated with real vector bundles that capture topological information about the bundle’s curvature and play a central role in differential topology and geometry.
- 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_69ca835481fc819084e33d3bc883bfa6 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5825385081908dee42cba8e98392 |
completed | March 31, 2026, 11:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf28958ba481908383e31802ce2093 |
completed | April 3, 2026, 2:40 a.m. |
Created at: March 30, 2026, 6:33 p.m.