Triple
T5918360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | فاتن حمامة |
E131636
|
entity |
| Predicate | اللغة_الفنية |
P6520
|
FINISHED |
| Object | العربية المصرية |
—
|
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: العربية المصرية | Statement: [فاتن حمامة, اللغة_الفنية, العربية المصرية]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: اللغة_الفنية Context triple: [فاتن حمامة, اللغة_الفنية, العربية المصرية]
-
A.
literaryLanguage
Indicates that an entity is expressed, written, or communicated using a particular literary or standardized written language.
-
B.
languageOfPoetry
Indicates that a specified language is the language in which a given piece of poetry is written or expressed.
-
C.
partOfLanguage
Indicates that one linguistic element belongs to, is included within, or is a component of a particular language.
-
D.
linguisticField
Indicates that something pertains to or is associated with a particular area or subdiscipline within linguistics.
-
E.
linguisticFeature
chosen
Indicates a relationship where a linguistic property, pattern, or characteristic is attributed to or associated with a language-related entity (such as a word, phrase, or text).
- 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_69c0085a1ed08190a7e9a8b6323fd680 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c048fc112c8190b905bf561c9de096 |
completed | March 22, 2026, 7:54 p.m. |
| PD | Predicate disambiguation | batch_69c03352208c8190968efed05a9fd416 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:59 p.m.