Triple
T427487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | K2 |
E9638
|
entity |
| Predicate | hasNoWidelyUsedLocalName |
P13789
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [K2, hasNoWidelyUsedLocalName, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNoWidelyUsedLocalName Context triple: [K2, hasNoWidelyUsedLocalName, true]
-
A.
hasLocalName
Indicates that an entity is known by a specific name or designation within a particular local language, script, or regional context.
-
B.
hasGivenNameUsage
Indicates that an entity is associated with a particular way or context in which its given name is used.
-
C.
isUniqueWithinStandard
Indicates that an entity is the only one of its kind within the scope or constraints of a given standard or specification.
-
D.
hasLatinName
Indicates that an entity is associated with a specific Latin (scientific) name.
-
E.
nameUsedToAvoid
Indicates that one entity uses a particular name or designation in order to avoid another entity or an unwanted situation involving that entity.
- 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_69a2e801e1d48190b505d1dd336b52ac |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2eed7f3508190995dcd39586ed614 |
completed | Feb. 28, 2026, 1:34 p.m. |
| PD | Predicate disambiguation | batch_69a2edd7a3608190b8785c7b7205f6c1 |
completed | Feb. 28, 2026, 1:29 p.m. |
| PDg | Predicate description generation | batch_69a2eeb93584819082f23eff13e17c4f |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.