Triple
T19470843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John-Paul |
E487117
|
entity |
| Predicate | etymologyLanguageOfComponentJohn |
P506
|
FINISHED |
| Object | Hebrew via Latin and Greek |
—
|
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: Hebrew via Latin and Greek | Statement: [John-Paul, etymologyLanguageOfComponentJohn, Hebrew via Latin and Greek]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: etymologyLanguageOfComponentJohn Context triple: [John-Paul, etymologyLanguageOfComponentJohn, Hebrew via Latin and Greek]
-
A.
etymologyContext
Indicates the contextual or situational background (such as time, place, culture, or domain) relevant to the origin and historical development of a word or term.
-
B.
etymology
Indicates the historical origin and development of a word or term, including its source language and form.
-
C.
etymologicalLanguage
chosen
Indicates the language from which a word or term is historically derived in its etymology.
-
D.
etymologyType
Indicates the specific kind or category of etymological relationship that links a term to its linguistic origin or source.
-
E.
etymologyPossibleMeaning
Indicates a possible or hypothesized meaning that an etymological analysis suggests for a word or term.
- 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633e809008190b0021d41b99f9700 |
completed | April 20, 2026, 2:10 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7883308190b73912a71a35a835 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:39 p.m.