Triple
T18199869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hungarian orthography |
E435753
|
entity |
| Predicate | marksLongConsonantsBy |
P32655
|
FINISHED |
| Object | double letters |
—
|
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: double letters | Statement: [Hungarian orthography, marksLongConsonantsBy, double letters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marksLongConsonantsBy Context triple: [Hungarian orthography, marksLongConsonantsBy, double letters]
-
A.
hasConsonantLengthContrast
chosen
Indicates that a language distinguishes meaning between words based on differences in the length or duration of consonant sounds.
-
B.
hasConsonantSigns
Indicates that one entity possesses or includes consonant sign characters as part of its representation or structure.
-
C.
usesFinalConsonants
Indicates that an entity employs or contains consonant sounds in final position, such as at the end of a word, syllable, or phonological unit.
-
D.
usesToneMarks
Indicates that one entity applies or includes diacritical tone marks in the representation or transcription of another entity (such as text, language, or symbols).
-
E.
romanizesConsonant
Indicates that one system or process converts or represents a consonant from one writing system or script into its corresponding form in another script (typically a Latin-based romanization).
- 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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e0d610f88190b4f69b1c433ea6b1 |
completed | April 19, 2026, 2:04 p.m. |
| PD | Predicate disambiguation | batch_69e4331e92408190ad607ba4956a3897 |
completed | April 19, 2026, 1:42 a.m. |
Created at: April 10, 2026, 10:31 a.m.