Triple
T2079429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UNSC |
E45203
|
entity |
| Predicate | typeOfResolution |
P21258
|
FINISHED |
| Object | binding resolutions |
—
|
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: binding resolutions | Statement: [UNSC, typeOfResolution, binding resolutions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfResolution Context triple: [UNSC, typeOfResolution, binding resolutions]
-
A.
resolutionType
chosen
Indicates the specific manner or category by which an issue, event, or conflict is resolved or brought to a conclusion.
-
B.
typicalResolution
Indicates the usual or standard level of detail or clarity at which something (such as an image, display, or representation) is normally rendered or presented.
-
C.
resolutionClass
Indicates the category or type of resolution applied to address or conclude a particular issue, conflict, or process.
-
D.
hasResolution
Indicates that one entity possesses, specifies, or is associated with a particular level or type of resolution (such as detail, clarity, or granularity) in relation to another entity.
-
E.
basedOnResolution
Indicates that something is determined, derived, or decided according to a particular resolution, decision, or formal determination.
- 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_69a8891869c88190a02643e3bb746f59 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abba3307308190ab329fe3192b2e0f |
completed | March 7, 2026, 5:40 a.m. |
| PD | Predicate disambiguation | batch_69abb7b298a48190b4bdf7c9800b058d |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:41 p.m.