Triple
T21751162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Order of Cluny |
E536914
|
entity |
| Predicate | hasLanguage |
P15
|
FINISHED |
| Object | Latin |
—
|
NE NERFINISHED |
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: Latin | Statement: [Order of Cluny, hasLanguage, Latin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Latin Context triple: [Order of Cluny, hasLanguage, Latin]
-
A.
Latin
chosen
Latin is an ancient Italic language of the Roman Empire that profoundly shaped the vocabulary, grammar, and development of many European languages and scholarly traditions.
-
B.
Llatí
Llatí is a neighborhood within the municipality of Santa Coloma de Gramenet in the metropolitan area of Barcelona, Catalonia, Spain.
-
C.
Latin I
Latin I is an introductory course in the Latin language that typically covers basic grammar, vocabulary, and reading skills while introducing students to aspects of ancient Roman culture.
-
D.
Latin I
Latin I is a vowel letter of the modern Latin alphabet, commonly representing the /i/ sound in many languages.
-
E.
Latin Lingo
"Latin Lingo" is a bilingual hip hop track by Cypress Hill that showcases their signature fusion of English and Spanish lyrics over a gritty, funk-influenced beat.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c46eab808190b848242d63a17c47 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01d8a6d4881908cc69e7247cce3a5 |
completed | April 28, 2026, 2:38 a.m. |
Created at: April 16, 2026, 6:50 p.m.