Triple
T12428940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | النَّازِعَات |
E296971
|
entity |
| Predicate | من حيث عدد الكلمات |
P6006
|
FINISHED |
| Object | من السور القصيرة نسبياً |
—
|
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: من السور القصيرة نسبياً | Statement: [النَّازِعَات, من حيث عدد الكلمات, من السور القصيرة نسبياً]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: من حيث عدد الكلمات Context triple: [النَّازِعَات, من حيث عدد الكلمات, من السور القصيرة نسبياً]
-
A.
wordLength
Indicates that there is a relationship specifying the number of characters (length) in a given word.
-
B.
wordCount
Indicates the total number of words contained in a given text or linguistic unit.
-
C.
lengthInWords
chosen
Indicates the number of words that make up the length of something, typically a text or expression.
-
D.
wordLengthCategory
Indicates the categorical classification of a word based on its length (e.g., short, medium, long).
-
E.
hasNumberOfTerms
Indicates the quantity of distinct terms or elements associated with a given entity or expression.
- 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_69d6ada0640c81908c061d7fb3d47786 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94df948308190ace333230a4a3b38 |
completed | April 10, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_69d94d391c548190996a8c698357f273 |
completed | April 10, 2026, 7:19 p.m. |
Created at: April 8, 2026, 9:55 p.m.