Triple
T12939620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Office of the megas doux |
E309603
|
entity |
| Predicate | usedTitleLanguage |
P89513
|
FINISHED |
| Object | Medieval Greek |
—
|
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: Medieval Greek | Statement: [Office of the megas doux, usedTitleLanguage, Medieval Greek]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedTitleLanguage Context triple: [Office of the megas doux, usedTitleLanguage, Medieval Greek]
-
A.
titleInLanguage
chosen
Indicates that a specific title or name is expressed in a particular language.
-
B.
languageOfTitleHolder
Indicates the language in which the title held by a given title holder is expressed or recorded.
-
C.
titleInLocalLanguage
Indicates that an entity’s title is expressed in the primary or native language of a specified place or community.
-
D.
languageOfCatalogueTitle
Indicates the language in which a catalogue’s title is expressed.
-
E.
titleLanguageForm
Indicates the specific linguistic form or variant in which a title is expressed (e.g., language, script, or transliteration form).
- 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_69d7bdfa933c8190b5a27aa4a08a19b7 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e59a4c88190907d05b8d57dae89 |
completed | April 10, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69d97db69f548190a1a693bc0d6c191a |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 5:43 p.m.