Triple

T24017068
Position Surface form Disambiguated ID Type / Status
Subject European route E123 E594707 entity
Predicate hasJunctionWith P1018 FINISHED
Object European route E38
European route E38 is an international road in the E-road network that runs across parts of Eastern Europe and Central Asia, connecting cities in countries such as Ukraine, Russia, and Kazakhstan.
 E1648318 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: European route E38 | Statement: [European route E123, hasJunctionWith, European route E38]
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: European route E38
Triple: [European route E123, hasJunctionWith, European route E38]
Generated description
European route E38 is an international road in the E-road network that runs across parts of Eastern Europe and Central Asia, connecting cities in countries such as Ukraine, Russia, and Kazakhstan.

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_69e288bc8f608190ac4af29f0bd1c744 completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1d5a6123c8190871e10cb81dfa819 completed April 29, 2026, 9:55 a.m.
NED1 Entity disambiguation (via context triple) batch_6a100fce0c78819087842fb97d8e3b3c completed May 22, 2026, 8:11 a.m.
NEDg Description generation batch_6a10136871588190b4e4b4618ab7a400 completed May 22, 2026, 8:27 a.m.
NED2 Entity disambiguation (via description) batch_6a10140b2fec8190aa6d805f54926b56 completed May 22, 2026, 8:30 a.m.
Created at: April 17, 2026, 9:42 p.m.