Triple
T15475888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joninės |
E376778
|
entity |
| Predicate | RasosNameOrigin |
P3325
|
FINISHED |
| Object | derived from Lithuanian word for dew (rasa) |
—
|
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: derived from Lithuanian word for dew (rasa) | Statement: [Joninės, RasosNameOrigin, derived from Lithuanian word for dew (rasa)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: RasosNameOrigin Context triple: [Joninės, RasosNameOrigin, derived from Lithuanian word for dew (rasa)]
-
A.
nameEtymologyFor
Indicates that one entity expresses or explains the origin or derivation of the name of another entity.
-
B.
hasNameOrigin
chosen
Indicates that the origin or source of an entity’s name is specified by the related entity.
-
C.
JapaneseNameOrigin
Indicates that one entity’s name originates from or is derived from the Japanese language or naming tradition in relation to another entity.
-
D.
possibleNameEtymology
Indicates a hypothesized or suggested origin or derivation of an entity’s name from another term, source, or linguistic root.
-
E.
languageOfNamesake
Indicates the language in which the namesake of an entity (such as a person, place, or object) is named or expressed.
- 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_69d85cd21dcc81908646251b1c26ea00 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f88a5dc8190a2d7830748e29180 |
completed | April 16, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69ded2874b788190999158e0f043be21 |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:34 a.m.