Triple
T1040118
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tehran dialect |
E22450
|
entity |
| Predicate | hasLexicon |
P18536
|
FINISHED |
| Object | Tehrani Persian vocabulary |
—
|
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: Tehrani Persian vocabulary | Statement: [Tehran dialect, hasLexicon, Tehrani Persian vocabulary]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLexicon Context triple: [Tehran dialect, hasLexicon, Tehrani Persian vocabulary]
-
A.
lexiconStatus
Indicates the current state or condition of a lexical item within a lexicon, such as whether it is active, deprecated, provisional, or otherwise classified.
-
B.
hasKnownVocabulary
chosen
Indicates that an entity possesses a defined, identifiable set of terms or words that it can recognize or use.
-
C.
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.
-
D.
hasMajorDictionary
Indicates that an entity possesses or is associated with a primary or authoritative dictionary resource.
-
E.
hasDictionary
Indicates that one entity possesses, includes, or is associated with a dictionary resource.
- 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_69a493d91478819094cc01fb65564bc1 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b97c64a88190bf1119fdd4940bf3 |
completed | March 1, 2026, 10:11 p.m. |
| PD | Predicate disambiguation | batch_69a4b729f8488190b2042bd9c625a833 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:41 p.m.