Triple
T12668323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hail Holy Queen |
E302613
|
entity |
| Predicate | hasOpeningWordsEnglish |
P67696
|
FINISHED |
| Object | Hail, holy Queen, Mother of 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: Hail, holy Queen, Mother of mercy | Statement: [Hail Holy Queen, hasOpeningWordsEnglish, Hail, holy Queen, Mother of mercy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOpeningWordsEnglish Context triple: [Hail Holy Queen, hasOpeningWordsEnglish, Hail, holy Queen, Mother of mercy]
-
A.
hasEnglishIncipit
chosen
Indicates that an entity has an opening phrase or initial text (incipit) expressed in English.
-
B.
hasOpening
Indicates that one entity possesses or features an opening, gap, or entrance that allows access, passage, or exposure.
-
C.
hasEnglishReception
Indicates that something has been received, interpreted, or responded to within an English-speaking context.
-
D.
translationOfOpeningWords
Indicates that one text is a translation of the initial words or opening phrase of another text.
-
E.
hasOpeningVerseMeaning
Indicates that something (such as a text, song, or poem) possesses an opening verse that conveys a particular meaning or message.
- 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_69d7bded71a88190bb76e2413af9ea66 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961ae493481908f82e0d05dce20bd |
completed | April 10, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69d960bb64ec8190bd0400cf0cc8b0a7 |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:20 p.m.