Triple
T10991865
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Koebe quarter theorem |
E259770
|
entity |
| Predicate | sharpness |
P84550
|
FINISHED |
| Object | bound 1/4 is best possible |
—
|
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: bound 1/4 is best possible | Statement: [Koebe quarter theorem, sharpness, bound 1/4 is best possible]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sharpness Context triple: [Koebe quarter theorem, sharpness, bound 1/4 is best possible]
-
A.
sharpnessCondition
chosen
Indicates the condition or degree of sharpness that something possesses or is required to have.
-
B.
sharpeningType
Indicates the method or style by which something is sharpened or made sharper.
-
C.
precision
Indicates the degree to which an action, measurement, or outcome is carried out with exactness, minimal deviation, and fine-grained accuracy.
-
D.
resolution
Indicates the act of formally deciding, settling, or expressing a determined stance on an issue, often through an official decision or statement.
-
E.
hardness
Indicates the degree to which one entity resists being scratched, indented, or deformed by another.
- 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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d795d1e918819090c71f5a077fa15a |
completed | April 9, 2026, 12:04 p.m. |
| PD | Predicate disambiguation | batch_69d72e93ac648190b46c5d12bf3eb1e9 |
completed | April 9, 2026, 4:44 a.m. |
Created at: April 8, 2026, 9:24 p.m.