Triple

T32144148
Position Surface form Disambiguated ID Type / Status
Subject Libyan universities E820980 entity
Predicate include P1393 FINISHED
Object Omar Al-Mukhtar University
Omar Al-Mukhtar University is a major public higher education institution in Libya, known for offering a wide range of academic programs and contributing significantly to the country’s educational and research landscape.
 E1999926 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: Omar Al-Mukhtar University | Statement: [Libyan universities, include, Omar Al-Mukhtar University]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Omar Al-Mukhtar University
Triple: [Libyan universities, include, Omar Al-Mukhtar University]
Generated description
Omar Al-Mukhtar University is a major public higher education institution in Libya, known for offering a wide range of academic programs and contributing significantly to the country’s educational and research landscape.

Provenance (5 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_69f3490520d081909b2f1271dab75faa completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6b9b17d908190a6bfa2f8b5402f1e completed May 3, 2026, 2:57 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2f46bece588190b74565620650c175 completed June 15, 2026, 12:26 a.m.
NEDg Description generation batch_6a2f476da774819083632efe3902e9ec completed June 15, 2026, 12:29 a.m.
NED2 Entity disambiguation (via description) batch_6a2f47f1621c8190ab4af373fdf9eda8 completed June 15, 2026, 12:31 a.m.
Created at: May 1, 2026, 12:31 a.m.