Triple
T24756733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uneapa language |
E619306
|
entity |
| Predicate | hasGrammarProperty |
P103347
|
FINISHED |
| Object | serial verb constructions |
—
|
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: serial verb constructions | Statement: [Uneapa language, hasGrammarProperty, serial verb constructions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGrammarProperty Context triple: [Uneapa language, hasGrammarProperty, serial verb constructions]
-
A.
hasGrammar
Indicates that an entity possesses, follows, or is associated with a particular system of grammatical rules or structure.
-
B.
hasGrammarInformation
chosen
Indicates that an entity is associated with specific grammatical details or annotations, such as part of speech, inflection, or syntactic properties.
-
C.
hasKnownGrammar
Indicates that an entity is associated with a grammar whose structure and rules are already defined or understood.
-
D.
hasGrammarFrom
Indicates that one entity derives or uses its grammatical structure or rules from another entity.
-
E.
hasDistinctGrammar
Indicates that the subject’s grammar system is different in structure or rules from that of the object.
- 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_69e2fabb349881908a13a212a0221a63 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f44a417a58819081777e18dda149fd |
completed | May 1, 2026, 6:37 a.m. |
| PD | Predicate disambiguation | batch_69f442a977b08190b44eac040cb90211 |
completed | May 1, 2026, 6:05 a.m. |
Created at: April 18, 2026, 4:26 a.m.