Triple
T31740802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Archdiocese of Shillong |
E810133
|
entity |
| Predicate | hasLanguageOfPastoralCare |
P136634
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Archdiocese of Shillong, hasLanguageOfPastoralCare, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageOfPastoralCare Context triple: [Archdiocese of Shillong, hasLanguageOfPastoralCare, English]
-
A.
hasClericalLanguage
Indicates that something is expressed using formal, religious, or church-related language or terminology.
-
B.
hasLanguageOfPractice
Indicates that an entity uses or operates in a particular language as its regular or primary medium of practice.
-
C.
hasLanguageOfCaseReport
Indicates that a case report is expressed or written in a particular language.
-
D.
hasLanguageOfMission
chosen
Indicates that an entity (such as a mission or project) is associated with a specific language used for its communication, documentation, or operation.
-
E.
hasLanguageAspect
Indicates that an entity is associated with a particular linguistic aspect, such as tense, mood, or grammatical feature, in relation to a language.
- 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_69f348e233cc819083b6695f70cd75d8 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fe68a4b67881909ca1d9f276f922e0 |
completed | May 8, 2026, 10:50 p.m. |
| PD | Predicate disambiguation | batch_69fe680234c88190b01f953987b74972 |
completed | May 8, 2026, 10:47 p.m. |
Created at: April 30, 2026, 11:24 p.m.