Triple
T4814695
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Upsilon |
E107159
|
entity |
| Predicate | hasLatinEquivalent |
P2508
|
FINISHED |
| Object | Y |
—
|
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: Y | Statement: [Upsilon, hasLatinEquivalent, Y]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLatinEquivalent Context triple: [Upsilon, hasLatinEquivalent, Y]
-
A.
hasLatinName
Indicates that an entity is associated with a specific Latin (scientific) name.
-
B.
correspondsToLatinLetter
Indicates that one entity is the counterpart or representation of another entity as a specific letter in the Latin alphabet.
-
C.
hasRomanizationOf
chosen
Indicates that one entity is a romanized representation (written in the Latin alphabet) of the other entity’s original script form.
-
D.
hasCyrillicAlphabetForm
Indicates that an entity has a corresponding representation or form written in the Cyrillic alphabet.
-
E.
alternativeTransliteration
Indicates that one written form represents an alternative way of transliterating the same original text or name into another script or orthography.
- 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_69bd43f779448190b92885cb70abb6c2 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ddd17d881909f7731ff2b460e83 |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c1dfa3481909d240d50ed0ee38c |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:23 p.m.