Triple
T42392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Union Flag |
E835
|
entity |
| Predicate | officialProportion |
P3019
|
FINISHED |
| Object | 1:2 |
—
|
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: 1:2 | Statement: [Union Flag, officialProportion, 1:2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officialProportion Context triple: [Union Flag, officialProportion, 1:2]
-
A.
officialName
Indicates the formally recognized name assigned to an entity by an authoritative body or source.
-
B.
uniformDistinction
Indicates that a clear and consistent difference is maintained between two or more entities within a given context.
-
C.
officiallyReports
Indicates that one entity formally provides information, updates, or accountability to another entity within an official or authorized reporting structure.
-
D.
apportionedBy
Indicates that something is divided or allocated among parts or recipients according to a specified agent, rule, or method.
-
E.
employedApproximately
Indicates that one entity employs another in a manner where the number, duration, or extent of employment is approximate rather than exact.
- F. None of above. chosen
Provenance (4 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24c083ad081909c1122c8fb29efdc |
completed | Feb. 28, 2026, 1:59 a.m. |
| PD | Predicate disambiguation | batch_69a24aba9a2c81909f769a8f22e30c92 |
completed | Feb. 28, 2026, 1:54 a.m. |
| PDg | Predicate description generation | batch_69a24c0794c0819095509d970e05fc0f |
completed | Feb. 28, 2026, 1:59 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.