Triple
T443823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sinhala |
E10172
|
entity |
| Predicate | hasTenseSystem |
P5214
|
FINISHED |
| Object | past–non-past distinction |
—
|
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–non-past distinction | Statement: [Sinhala, hasTenseSystem, past–non-past distinction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTenseSystem Context triple: [Sinhala, hasTenseSystem, past–non-past distinction]
-
A.
hasTenseAspectSystem
chosen
Indicates that a language or clause employs a particular system for expressing tense and aspect distinctions.
-
B.
hasTense
Indicates that an action, event, or state is associated with a specific grammatical tense (such as past, present, or future).
-
C.
hasPastTenseEnding
Indicates that a verb form ends with a morphological marker typically used to express past tense.
-
D.
hasNounClassSystem
Indicates that an entity possesses a grammatical system in which nouns are categorized into distinct classes that affect their agreement with other elements in the language.
-
E.
hasFutureTenseEnding
Indicates that a verb or expression carries a morphological ending marking future 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_69a2e8465ef481909655c681b01e2986 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2ef43e8f88190a5d368add11a38c0 |
completed | Feb. 28, 2026, 1:36 p.m. |
| PD | Predicate disambiguation | batch_69a2edde2b9c8190bd20b582eb4c5065 |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.