Triple
T8330144
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Codex Laudianus (E 08) |
E195053
|
entity |
| Predicate | bilingualLayout |
P82135
|
FINISHED |
| Object | Greek and Latin in parallel columns |
—
|
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: Greek and Latin in parallel columns | Statement: [Codex Laudianus (E 08), bilingualLayout, Greek and Latin in parallel columns]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bilingualLayout Context triple: [Codex Laudianus (E 08), bilingualLayout, Greek and Latin in parallel columns]
-
A.
isBilingual
Indicates that an entity is able to communicate fluently in two distinct languages.
-
B.
bilingualName
Indicates that an entity has a name expressed in two different languages, linking the entity to its bilingual designation.
-
C.
isBilingualRegion
Indicates that a region officially uses two languages or has two predominant languages in regular use.
-
D.
usesBilingualInstruction
Indicates that an entity employs two languages as the medium of instruction within an educational or communicative context.
-
E.
hasLanguageOnSides
Indicates that an object or medium features written or spoken language present on multiple sides or surfaces.
- F. None of above. chosen
Provenance (4 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_69ca82e87f2c8190bdb71ee29dfc642d |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7fb995508190b2ca94ad45bf6d24 |
completed | March 31, 2026, 8:03 a.m. |
| PD | Predicate disambiguation | batch_69cb70c3231c81909e3d463192c9de22 |
completed | March 31, 2026, 6:59 a.m. |
| PDg | Predicate description generation | batch_69cb76d823b08190a54fadb50660cda5 |
completed | March 31, 2026, 7:25 a.m. |
Created at: March 30, 2026, 5:56 p.m.