Triple
T37775038
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BYHMC |
E941659
|
entity |
| Predicate | usesTransliteration |
P193694
|
FINISHED |
| Object | Babyn Yar |
—
|
NE NERFINISHED |
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: Babyn Yar | Statement: [BYHMC, usesTransliteration, Babyn Yar]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesTransliteration Context triple: [BYHMC, usesTransliteration, Babyn Yar]
-
A.
hasTransliterationRole
Indicates that an entity participates in a transliteration process with a specific role (e.g., source, target, or agent of transliteration).
-
B.
transliterationType
Indicates the specific system or method used to convert text from one writing system into another using corresponding characters.
-
C.
standardTransliteration
Indicates that one representation of text is a transliteration of another according to a recognized standard or convention.
-
D.
alternativeTransliteration
Indicates that one written form represents an alternative way of transliterating the same original text or name into another script or orthography.
-
E.
commonTransliterationSystem
Indicates that two or more written forms are derived using the same standardized system for converting text from one script to another.
- 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_69f76ee4431881908f87e8892a9f39f3 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fd4f39b5008190b83b3227ce22c509 |
completed | May 8, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_69fd4df17c548190a4e2a6fea70f7e10 |
completed | May 8, 2026, 2:44 a.m. |
| PDg | Predicate description generation | batch_69fd4f38728c8190b3271abc80882cfb |
completed | May 8, 2026, 2:49 a.m. |
Created at: May 3, 2026, 4:19 p.m.