Triple
T28807192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bodmer Papyrus XIV–XV |
E727410
|
entity |
| Predicate | containsCanonicalText |
P7166
|
FINISHED |
| Object | canonical Gospels |
—
|
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: canonical Gospels | Statement: [Bodmer Papyrus XIV–XV, containsCanonicalText, canonical Gospels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsCanonicalText Context triple: [Bodmer Papyrus XIV–XV, containsCanonicalText, canonical Gospels]
-
A.
hasCanonicalCharacter
Indicates that something is associated with or defined by its standard, officially recognized character representation.
-
B.
hasCanonicalReference
Indicates that one entity serves as the authoritative or standard reference source for another entity.
-
C.
hasCanonicalRepresentation
Indicates that one entity is the standard or authoritative form in which another entity is represented.
-
D.
containsText
Indicates that one entity includes the specified text string within its content.
-
E.
hasText
chosen
Indicates that an entity is associated with or contains a specific piece of textual content.
- 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_69f0319c38948190bca746ad60fd25ba |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_69fdd07a34c08190982b8c61c2775cf6 |
completed | May 8, 2026, noon |
| PD | Predicate disambiguation | batch_69fdbd25c7908190b72fca8de7ce503f |
completed | May 8, 2026, 10:38 a.m. |
Created at: April 28, 2026, 6:29 a.m.