Triple
T27415027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Piapoco |
E692867
|
entity |
| Predicate | hasWordOrderFlexibility |
P180401
|
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: [Piapoco, hasWordOrderFlexibility, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWordOrderFlexibility Context triple: [Piapoco, hasWordOrderFlexibility, true]
-
A.
hasBasicWordOrder
Indicates the typical sequence in which core sentence elements (such as subject, verb, and object) are ordered in a language.
-
B.
alsoExhibitsWordOrder
Indicates that one linguistic element displays the same or an additional word order pattern as another element or construction.
-
C.
hasV2WordOrder
Indicates that a clause or language follows verb-second (V2) word order, where the finite verb consistently appears in the second position of the sentence.
-
D.
isInflectedLanguage
Indicates that a language uses systematic changes in word form (inflections) to express grammatical categories such as tense, case, number, or gender.
-
E.
haveGrammaticalTrait
Indicates that an entity possesses a specific grammatical property or feature, such as tense, number, gender, or case.
- 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_69ef5208617081908f731d312e0fd1bc |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f74062b9388190b30546cf700a825c |
completed | May 3, 2026, 12:32 p.m. |
| PD | Predicate disambiguation | batch_69f73c802b848190b61a416b7488bd96 |
completed | May 3, 2026, 12:16 p.m. |
| PDg | Predicate description generation | batch_69f74061c440819080434155c2d60341 |
completed | May 3, 2026, 12:32 p.m. |
Created at: April 27, 2026, 12:33 p.m.