Triple
T29586736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Book VII: Processes |
E754039
|
entity |
| Predicate | belongsToLegalCorpus |
P91332
|
FINISHED |
| Object | Latin Code of Canon Law (1983) |
—
|
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 Code of Canon Law (1983) | Statement: [Book VII: Processes, belongsToLegalCorpus, Latin Code of Canon Law (1983)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: belongsToLegalCorpus Context triple: [Book VII: Processes, belongsToLegalCorpus, Latin Code of Canon Law (1983)]
-
A.
hasPartOfCorpus
Indicates that one entity constitutes a component or segment of the overall corpus associated with another entity.
-
B.
legalCorpus
chosen
Indicates that an entity is part of, contained in, or belongs to a body of legal texts, statutes, or case law used as a legal corpus.
-
C.
corpus
Indicates that an entity is a collection or body of texts, documents, or linguistic data used as a unified set for analysis or reference.
-
D.
hasCorpusType
Indicates the type or category of corpus associated with an entity (e.g., text, speech, multimodal).
-
E.
hasNotableCorpus
Indicates that an entity possesses a significant, well-recognized body of work, texts, or collected materials associated with it.
- 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_69f0ef836ac88190bd809dc58b5ec907 |
completed | April 28, 2026, 5:33 p.m. |
| NER | Named-entity recognition | batch_69fd7fdafbe881908a31fcb407af2c34 |
completed | May 8, 2026, 6:16 a.m. |
| PD | Predicate disambiguation | batch_69fd7ef0ea908190b5d83f71565bdb1c |
completed | May 8, 2026, 6:13 a.m. |
Created at: April 28, 2026, 6:11 p.m.