Triple

T24074863
Position Surface form Disambiguated ID Type / Status
Subject Egyptian military airbase network E596331 entity
Predicate hasComponent P35 FINISHED
Object Tanta Air Base
Tanta Air Base is a key Egyptian Air Force installation located near the city of Tanta in the Nile Delta, supporting military aviation operations and regional defense.
E1646479 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: Tanta Air Base | Statement: [Egyptian military airbase network, hasComponent, Tanta Air Base]
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: Tanta Air Base
Triple: [Egyptian military airbase network, hasComponent, Tanta Air Base]
Generated description
Tanta Air Base is a key Egyptian Air Force installation located near the city of Tanta in the Nile Delta, supporting military aviation operations and regional defense.

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_69e288c3999c8190809b282a04813dec completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1db1c912c8190ae6438a84fc51d11 completed April 29, 2026, 10:19 a.m.
NED1 Entity disambiguation (via context triple) batch_6a100fd0268481909db61370fe294aa2 completed May 22, 2026, 8:12 a.m.
NEDg Description generation batch_6a10138b45648190ba35124148ba7cf2 completed May 22, 2026, 8:27 a.m.
NED2 Entity disambiguation (via description) batch_6a10143c5c84819081dd4f953fa9841a completed May 22, 2026, 8:30 a.m.
Created at: April 17, 2026, 10:42 p.m.