Triple
T4030571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thorney Abbey (translated relics) |
E83698
|
entity |
| Predicate | hasLanguageOfCult |
P45954
|
FINISHED |
| Object | Latin |
—
|
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: Latin | Statement: [Thorney Abbey (translated relics), hasLanguageOfCult, Latin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageOfCult Context triple: [Thorney Abbey (translated relics), hasLanguageOfCult, Latin]
-
A.
isCulturalLanguageOf
Indicates that a language serves as a primary medium of cultural expression, identity, and heritage for a particular group, community, or region.
-
B.
isLanguageOf
Indicates that a particular language is used as the official or primary language associated with a given entity (such as a person, document, or region).
-
C.
hasLanguageOn
Indicates that an entity uses or is associated with a particular language in a specific context, medium, or location.
-
D.
hasLanguageOfOrigin
Indicates that one entity has its origin or source in the language specified by another entity.
-
E.
hasClericalLanguage
chosen
Indicates that something is expressed using formal, religious, or church-related language or terminology.
- 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_69aed92e29ac819080f7a98b594fec05 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefaf1d8208190951a20ad7e5ab7bc |
completed | March 9, 2026, 4:53 p.m. |
| PD | Predicate disambiguation | batch_69aef8fe440c819093a7fa22c4ff3f1a |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:36 p.m.