Triple
T34396545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kyrie |
E882845
|
entity |
| Predicate | meaningOfTitleInEnglish |
P4542
|
FINISHED |
| Object | Lord, have mercy |
—
|
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: Lord, have mercy | Statement: [Kyrie, meaningOfTitleInEnglish, Lord, have mercy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: meaningOfTitleInEnglish Context triple: [Kyrie, meaningOfTitleInEnglish, Lord, have mercy]
-
A.
titleInEnglish
Indicates that an entity’s title or name is given in the English language.
-
B.
titleMeaning
chosen
Indicates that one entity expresses or explains the meaning, significance, or interpretation of another entity’s title.
-
C.
titleLanguageMeaning
Indicates that a title is expressed in a particular language and conveys a specific meaning in that language.
-
D.
hasTitleInModernEnglish
Indicates that an entity is associated with a specific title expressed in modern English.
-
E.
equivalentTitleInEngland
Indicates that one title corresponds to an equivalent or matching title within the context of England’s system of titles.
- 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_69f349c1304081909331872829e38106 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71c35327c8190884f1bfe12bd2cd7 |
completed | May 3, 2026, 9:58 a.m. |
| PD | Predicate disambiguation | batch_69f71822d0e88190ac9731c7ae5a4def |
completed | May 3, 2026, 9:40 a.m. |
Created at: May 1, 2026, 1:59 a.m.