Triple
T13582979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | سورة الطارق |
E324469
|
entity |
| Predicate | لغة |
P4185
|
FINISHED |
| Object | اللغة العربية |
E1330
|
NE 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: اللغة العربية | Statement: [سورة الطارق, لغة, اللغة العربية]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: اللغة العربية Context triple: [سورة الطارق, لغة, اللغة العربية]
-
A.
Arabic
chosen
Arabic is a Semitic language widely spoken across the Arab world and used as a liturgical language in Islam.
-
B.
al-Lughāt
al-Lughāt is a significant early Arabic linguistic work by the grammarian Al-Farrāʾ, focusing on vocabulary, dialectal usage, and philological analysis.
-
C.
Arabhi
Arabhi is a bright and energetic Carnatic raga, often used in devotional compositions and known for its auspicious, uplifting character.
-
D.
Lisān al-ʿArab
Lisān al-ʿArab is a monumental 13th-century Arabic dictionary by Ibn Manẓūr, renowned as one of the most comprehensive and authoritative works on classical Arabic vocabulary and usage.
-
E.
Persian language
Persian language is a major modern Iranian language spoken primarily in Iran, Afghanistan, and Tajikistan, known for its rich literary tradition and historical influence across the Middle East and Central Asia.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d80769100c819099111274614f5ed2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb031e8048190a5f2ea934308036c |
completed | April 12, 2026, 2:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f76bbf946c8190ba3d2b87cb11dc9d |
completed | May 3, 2026, 3:37 p.m. |
Created at: April 9, 2026, 9:49 p.m.