Triple
T19979338
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old English Hexateuch |
E493772
|
entity |
| Predicate | textualType |
P5468
|
FINISHED |
| Object | vernacular paraphrase and translation |
—
|
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: vernacular paraphrase and translation | Statement: [Old English Hexateuch, textualType, vernacular paraphrase and translation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: textualType Context triple: [Old English Hexateuch, textualType, vernacular paraphrase and translation]
-
A.
textType
chosen
Indicates the classification of a text according to its type, format, or genre.
-
B.
linguisticType
Indicates the type or category of language or linguistic system associated with an entity (e.g., spoken, signed, written, or other linguistic modality).
-
C.
plaintextType
Indicates that the associated content or data is represented in an unencrypted, human-readable text format.
-
D.
textuallySystematizedBy
Indicates that information, concepts, or data are organized, structured, or codified into a systematic textual form by a particular agent or source.
-
E.
textualAppearance
Indicates how something is presented, formatted, or visually structured in written or printed 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65d11d2108190bd1d91fdc834888b |
completed | April 20, 2026, 5:06 p.m. |
| PD | Predicate disambiguation | batch_69e537fae79c81909eae39500766d0b6 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 11, 2026, 3:27 p.m.