Triple
T6646399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lule Sami language |
E150710
|
entity |
| Predicate | hasNumberCategories |
P4426
|
FINISHED |
| Object | singular and plural |
—
|
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: singular and plural | Statement: [Lule Sami language, hasNumberCategories, singular and plural]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberCategories Context triple: [Lule Sami language, hasNumberCategories, singular and plural]
-
A.
hasNumberCategory
chosen
Indicates that an entity is associated with a specific numerical classification or type.
-
B.
hasCategoryNumbering
Indicates that an entity is assigned or associated with a specific category-based numbering or index within a classification system.
-
C.
hasCategoryCount
Indicates the number of distinct categories associated with a given entity.
-
D.
hasFrequencyCategory
Indicates that something is associated with a particular classification of how often it occurs or is used.
-
E.
hasApproximateNumberOfVarieties
Indicates that an entity is associated with an estimated or non-exact count of different varieties or types.
- 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_69c687f1a3048190828b7342f7125d5c |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6cc9c6cb0819084fec8e0beb430de |
completed | March 27, 2026, 6:29 p.m. |
| PD | Predicate disambiguation | batch_69c6ad04d66c8190926ffcbff372643b |
completed | March 27, 2026, 4:15 p.m. |
Created at: March 27, 2026, 2 p.m.