Triple
T3619974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yafiʿi Arabic |
E76698
|
entity |
| Predicate | hasLexiconType |
P23102
|
FINISHED |
| Object | variety of Yemeni Arabic lexicon |
—
|
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: variety of Yemeni Arabic lexicon | Statement: [Yafiʿi Arabic, hasLexiconType, variety of Yemeni Arabic lexicon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLexiconType Context triple: [Yafiʿi Arabic, hasLexiconType, variety of Yemeni Arabic lexicon]
-
A.
hasLanguageType
Indicates that an entity is associated with a particular type or category of language (e.g., spoken, written, programming, sign).
-
B.
hasDictionaryType
chosen
Indicates that something is associated with or classified under a specific dictionary-defined type or category.
-
C.
hasCorpusType
Indicates the type or category of corpus associated with an entity (e.g., text, speech, multimodal).
-
D.
hasLinguisticElement
Indicates that one entity includes, is associated with, or is characterized by a particular linguistic component such as a word, phrase, symbol, or other language element.
-
E.
hasPhonologicalType
Indicates that one entity is characterized by or classified as having a particular phonological type (e.g., in terms of sound structure or phonological category).
- 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_69ad85dae2fc81908d1ceadbc6af0089 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc2b85f6c819091000e231d9dac87 |
completed | March 8, 2026, 6:40 p.m. |
| PD | Predicate disambiguation | batch_69adb83f1e4c8190ab501c1c05b14c08 |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:23 p.m.