Triple
T404756
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gospel of Mark |
E9358
|
entity |
| Predicate | textualFeature |
P7162
|
FINISHED |
| Object | shorter ending at Mark 16:8 in earliest manuscripts |
—
|
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: shorter ending at Mark 16:8 in earliest manuscripts | Statement: [Gospel of Mark, textualFeature, shorter ending at Mark 16:8 in earliest manuscripts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: textualFeature Context triple: [Gospel of Mark, textualFeature, shorter ending at Mark 16:8 in earliest manuscripts]
-
A.
linguisticFeature
Indicates a relationship where a linguistic property, pattern, or characteristic is attributed to or associated with a language-related entity (such as a word, phrase, or text).
-
B.
featuresText
Indicates that an entity includes or presents a specific piece of text as one of its characteristics or contents.
-
C.
textType
Indicates the classification of a text according to its type, format, or genre.
-
D.
hasLinguisticFeature
chosen
Indicates that an entity possesses a particular linguistic property, trait, or characteristic.
-
E.
textFragment
Indicates that one piece of text is a constituent part or segment of a larger text.
- 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_69a2e8004cb88190b92ed1add6abf41a |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2eca37fe881909802126952dfdd59 |
completed | Feb. 28, 2026, 1:24 p.m. |
| PD | Predicate disambiguation | batch_69a2e97066e8819083cc1b3a421b9650 |
completed | Feb. 28, 2026, 1:11 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.