Triple
T220570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taíno |
E4202
|
entity |
| Predicate | loanwordInEnglish |
P506
|
FINISHED |
| Object | hurricane |
—
|
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: hurricane | Statement: [Taíno, loanwordInEnglish, hurricane]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: loanwordInEnglish Context triple: [Taíno, loanwordInEnglish, hurricane]
-
A.
hasCommonLoanwordsFrom
Indicates that two languages share loanwords that originate from the same source language.
-
B.
etymologicalLanguage
chosen
Indicates the language from which a word or term is historically derived in its etymology.
-
C.
lexicalChange
Indicates a relationship where one linguistic form is replaced, modified, or evolves into another form over time or across language varieties.
-
D.
lexicalItem
Indicates that one entity is a word or vocabulary unit associated with, or used to express, another entity (such as a concept, meaning, or linguistic entry).
-
E.
coinedTerm
Indicates that an entity originated and introduced a particular term or expression into use.
- 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_69a2573508588190b522c2476d91acfe |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25efd0df48190b8fef4c422a1265f |
completed | Feb. 28, 2026, 3:20 a.m. |
| PD | Predicate disambiguation | batch_69a25b54d790819093b35bd1a6f00f92 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.