Triple
T285482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Modern Greek |
E5876
|
entity |
| Predicate | hasOrthographicReform |
P10346
|
FINISHED |
| Object | monotonic orthography |
—
|
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: monotonic orthography | Statement: [Modern Greek, hasOrthographicReform, monotonic orthography]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOrthographicReform Context triple: [Modern Greek, hasOrthographicReform, monotonic orthography]
-
A.
hasOfficialOrthography
Indicates that an entity has a formally recognized and standardized system for writing its language or name.
-
B.
hasStandardOrthographySince
Indicates that a language or writing system has used a particular standardized orthography starting from a specified point in time.
-
C.
hasStandardPronunciationBasedOn
Indicates that one entity’s standard or canonical pronunciation is determined or derived from another entity’s pronunciation.
-
D.
hasRomanizationOf
Indicates that one entity is a romanized representation (written in the Latin alphabet) of the other entity’s original script form.
-
E.
usesDiacritics
Indicates that the referenced text or linguistic element employs diacritical marks as part of its written form.
- 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_69a25946a7ac8190a78871c210213272 |
completed | Feb. 28, 2026, 2:56 a.m. |
| NER | Named-entity recognition | batch_69a2605b372c8190831570aa6532cc96 |
completed | Feb. 28, 2026, 3:26 a.m. |
| PD | Predicate disambiguation | batch_69a25b7a8d148190aacdcc8ccb35c7f3 |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a2605a3d988190a8872169fd8eb2e8 |
completed | Feb. 28, 2026, 3:26 a.m. |
Created at: Feb. 28, 2026, 3:02 a.m.