Triple
T25954284
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Obelisk of Theodosius |
E654052
|
entity |
| Predicate | bilingualInscriptionLanguages |
P78564
|
FINISHED |
| Object | Greek and Latin |
—
|
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 | Statement: [Obelisk of Theodosius, bilingualInscriptionLanguages, Greek and Latin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bilingualInscriptionLanguages Context triple: [Obelisk of Theodosius, bilingualInscriptionLanguages, Greek and Latin]
-
A.
inscriptionsLanguage
Indicates that the language used in the inscriptions on an object or surface is the specified language.
-
B.
secondaryLanguageOfInscriptions
chosen
Indicates that a specified language serves as the secondary language used in the inscriptions associated with a given entity.
-
C.
officialLanguageOfInscriptions
Indicates the language officially used in the inscriptions associated with a particular entity.
-
D.
bellInscriptionLanguage
Indicates the language in which the inscription on a bell is written.
-
E.
transliterationOfInscription
Indicates that one text is a direct transliteration of the content of an inscription, preserving its original characters or script in another writing system.
- 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_69e7ab40ac788190a771bc499eb1ae5f |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f6049a5508819085be78ba6fbbfd69 |
completed | May 2, 2026, 2:05 p.m. |
| PD | Predicate disambiguation | batch_69f4a10480748190a2e67bd399fc435d |
completed | May 1, 2026, 12:48 p.m. |
Created at: April 22, 2026, 8:44 a.m.