Triple

T27693529
Position Surface form Disambiguated ID Type / Status
Subject M17 highway E698220 entity
Predicate partOf P40 FINISHED
Object European route E73
European route E73 is an important north–south trans-European road corridor connecting Hungary, Croatia, and Bosnia and Herzegovina to the Adriatic Sea.
 E1813972 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 E73 | Statement: [M17 highway, partOf, European route E73]
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 E73
Triple: [M17 highway, partOf, European route E73]
Generated description
European route E73 is an important north–south trans-European road corridor connecting Hungary, Croatia, and Bosnia and Herzegovina to the Adriatic Sea.

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_69ef590ea74081908f0cd7500d85fa27 completed April 27, 2026, 12:39 p.m.
NER Named-entity recognition batch_69f635798ac88190a8064ecac0f27e07 completed May 2, 2026, 5:33 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1627876c5c819095226c78d8afb7ac completed May 26, 2026, 11:06 p.m.
NEDg Description generation batch_6a16290c6a808190817f7bee27d4e0ee completed May 26, 2026, 11:13 p.m.
NED2 Entity disambiguation (via description) batch_6a162a28a0bc81909d87cabc75fdb1c3 completed May 26, 2026, 11:18 p.m.
Created at: April 27, 2026, 2:53 p.m.