Triple
T791189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | White Ensign |
E16917
|
entity |
| Predicate | featuresCanton |
P19326
|
FINISHED |
| Object | Union Flag in the canton |
—
|
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: Union Flag in the canton | Statement: [White Ensign, featuresCanton, Union Flag in the canton]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresCanton Context triple: [White Ensign, featuresCanton, Union Flag in the canton]
-
A.
hasCanton
Indicates that an entity is administratively divided into, or associated with, a specific canton.
-
B.
includesCanton
Indicates that a larger administrative or geographic entity contains or encompasses a specific canton within its boundaries.
-
C.
canton
Indicates that an entity is administratively located within, belongs to, or is governed as part of a specific canton.
-
D.
cantonDesign
Indicates that a canton (administrative region) is responsible for designing or determining the form, structure, or layout of something.
-
E.
cantonPosition
Indicates the relative placement or arrangement of a canton within a larger geographic or administrative context.
- 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_69a4936cb7448190914f5fe4b8d81607 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a79754988190ab494b1c54d6a2a4 |
completed | March 1, 2026, 8:54 p.m. |
| PD | Predicate disambiguation | batch_69a4a50ef72c819084ffe9f31dbd0262 |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a62b497081909503c8d30c7ce1db |
completed | March 1, 2026, 8:48 p.m. |
Created at: March 1, 2026, 7:38 p.m.