Triple
T6646400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lule Sami language |
E150710
|
entity |
| Predicate | hasTenseDistinctions |
P5214
|
FINISHED |
| Object | past and non-past |
—
|
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: past and non-past | Statement: [Lule Sami language, hasTenseDistinctions, past and non-past]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTenseDistinctions Context triple: [Lule Sami language, hasTenseDistinctions, past and non-past]
-
A.
hasTense
Indicates that an action, event, or state is associated with a specific grammatical tense (such as past, present, or future).
-
B.
hasTenseAspectSystem
chosen
Indicates that a language or clause employs a particular system for expressing tense and aspect distinctions.
-
C.
hasTenseAspect
Indicates that a verb or clause is associated with a specific grammatical tense and aspect configuration.
-
D.
hasDefinitenessDistinction
Indicates that a language or system grammatically distinguishes between definite and indefinite (or otherwise specified) reference in its expressions.
-
E.
hasPastTenseEnding
Indicates that a verb form ends with a morphological marker typically used to express past tense.
- 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.