Triple

T28221810
Position Surface form Disambiguated ID Type / Status
Subject Boeing Commercial Airplanes production network E711475 entity
Predicate includesFacilityAt P168281 FINISHED
Object Boeing Sheffield
Boeing Sheffield is a UK-based manufacturing facility that produces high-tech components for Boeing’s commercial aircraft programs.
 E1807258 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: Boeing Sheffield | Statement: [Boeing Commercial Airplanes production network, includesFacilityAt, Boeing Sheffield]
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: Boeing Sheffield
Triple: [Boeing Commercial Airplanes production network, includesFacilityAt, Boeing Sheffield]
Generated description
Boeing Sheffield is a UK-based manufacturing facility that produces high-tech components for Boeing’s commercial aircraft programs.

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_69efb51dfb048190ada79b745c33b363 completed April 27, 2026, 7:12 p.m.
NER Named-entity recognition batch_69fed1049564819091cf5908526e4679 completed May 9, 2026, 6:15 a.m.
NED1 Entity disambiguation (via context triple) batch_6a15e6c74ccc8190ad6ddfbf16f8a5b9 completed May 26, 2026, 6:30 p.m.
NEDg Description generation batch_6a15e84677308190bce7befb84a7f051 completed May 26, 2026, 6:36 p.m.
NED2 Entity disambiguation (via description) batch_6a15e88498888190b846228954d21189 completed May 26, 2026, 6:37 p.m.
Created at: April 27, 2026, 10:47 p.m.