Triple
T22047947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bidayuhic languages |
E544809
|
entity |
| Predicate | haveLexicalSimilarity |
P11829
|
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: [Bidayuhic languages, haveLexicalSimilarity, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: haveLexicalSimilarity Context triple: [Bidayuhic languages, haveLexicalSimilarity, true]
-
A.
hasLexicalSimilarityWith
chosen
Indicates that two linguistic items share a significant degree of similarity in form, structure, or wording.
-
B.
hasSimilarityTo
Indicates that one entity shares common characteristics, features, or qualities with another entity to a notable degree.
-
C.
hasGrammaticalSimilarityTo
Indicates that two linguistic elements share similar grammatical structure, form, or function.
-
D.
hasLetterSetSimilarity
Indicates that two entities share a similar set of letters, typically based on overlap or resemblance between the characters in their textual representations.
-
E.
hasLexicalDifferencesWith
Indicates that two linguistic items differ from each other in their word choice or lexical form.
- 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_69e11e32445c8190ab97089b48a130bb |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f12830c674819080254d77ee02bc9f |
completed | April 28, 2026, 9:35 p.m. |
| PD | Predicate disambiguation | batch_69e6f643ca74819083e8ab78e843f243 |
completed | April 21, 2026, 4 a.m. |
Created at: April 16, 2026, 8:26 p.m.