Triple

T29817574
Position Surface form Disambiguated ID Type / Status
Subject Lahti railway station E757152 entity
Predicate railwayLine P848 FINISHED
Object Lahti–Loviisa railway
The Lahti–Loviisa railway is a Finnish rail line connecting the inland city of Lahti with the coastal town of Loviisa, historically used for both passenger and freight transport.
E1888304 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: Lahti–Loviisa railway | Statement: [Lahti railway station, railwayLine, Lahti–Loviisa railway]
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: Lahti–Loviisa railway
Triple: [Lahti railway station, railwayLine, Lahti–Loviisa railway]
Generated description
The Lahti–Loviisa railway is a Finnish rail line connecting the inland city of Lahti with the coastal town of Loviisa, historically used for both passenger and freight transport.

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_69f2245701c88190ad42415a0956c4ed completed April 29, 2026, 3:31 p.m.
NER Named-entity recognition batch_69f675642e3c819098f45ff76b60355f completed May 2, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26f1bb3ac88190a307260a245def80 completed June 8, 2026, 4:45 p.m.
NEDg Description generation batch_6a26f28951d0819093b834f08eff940b completed June 8, 2026, 4:49 p.m.
NED2 Entity disambiguation (via description) batch_6a26f365e1448190bc8539feec582fd7 completed June 8, 2026, 4:52 p.m.
Created at: April 29, 2026, 5:27 p.m.