Triple
T38609205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ЗА ОБОРОНУ КАВКАЗА |
E934430
|
entity |
| Predicate | translatedInscription |
P5528
|
FINISHED |
| Object | For the Defense of the Caucasus |
—
|
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: For the Defense of the Caucasus | Statement: [ЗА ОБОРОНУ КАВКАЗА, translatedInscription, For the Defense of the Caucasus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: translatedInscription Context triple: [ЗА ОБОРОНУ КАВКАЗА, translatedInscription, For the Defense of the Caucasus]
-
A.
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.
-
B.
inscriptionTranslation
chosen
Indicates that a provided text expresses the translated content of a specific inscription.
-
C.
reverseInscriptionMeaning
Indicates that the inscription’s meaning is the reverse or opposite of the usual or expected interpretation.
-
D.
scriptOfInscription
Indicates the writing system or script in which a given inscription is written.
-
E.
inscriptionsLanguage
Indicates that the language used in the inscriptions on an object or surface is the specified language.
- 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_69f76eccd6d081909ccce171011739a1 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a000a1d8fa88190a1d82ac746565c48 |
completed | May 10, 2026, 4:31 a.m. |
| PD | Predicate disambiguation | batch_6a0008b26eb88190ae03b2309a614774 |
completed | May 10, 2026, 4:25 a.m. |
Created at: May 3, 2026, 4:32 p.m.