Triple
T135443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Romanian language |
E2736
|
entity |
| Predicate | hasWordOrder |
P1249
|
FINISHED |
| Object | SVO |
—
|
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: SVO | Statement: [Romanian language, hasWordOrder, SVO]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWordOrder Context triple: [Romanian language, hasWordOrder, SVO]
-
A.
hasBasicWordOrder
chosen
Indicates the typical sequence in which core sentence elements (such as subject, verb, and object) are ordered in a language.
-
B.
hasOrder
Indicates that one entity possesses, is associated with, or is characterized by a specific order, sequence, or arrangement relative to others.
-
C.
hasWritingDirection
Indicates the direction in which writing or text is read or written for a given script, language, or text system.
-
D.
orderedBy
Indicates that one entity has placed a request or purchase for another entity, typically specifying who initiated the order.
-
E.
hasCommonLoanwordsFrom
Indicates that two languages share loanwords that originate from the same source language.
- 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_69a2520c0f3481908b0ed054a2fca8d0 |
completed | Feb. 28, 2026, 2:25 a.m. |
| NER | Named-entity recognition | batch_69a257a3ad908190b6a8652f09ae0cbb |
completed | Feb. 28, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_69a25651b9048190a6277b7fec98c1ea |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:30 a.m.