Triple

T36673580
Position Surface form Disambiguated ID Type / Status
Subject Lecce railway station E905482 entity
Predicate railwayLine P848 FINISHED
Object Lecce–Novoli railway
The Lecce–Novoli railway is a regional rail line in Italy that connects the city of Lecce with the nearby town of Novoli, serving local passenger and commuter traffic in the Apulia region.
E2197227 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: Lecce–Novoli railway | Statement: [Lecce railway station, railwayLine, Lecce–Novoli 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: Lecce–Novoli railway
Triple: [Lecce railway station, railwayLine, Lecce–Novoli railway]
Generated description
The Lecce–Novoli railway is a regional rail line in Italy that connects the city of Lecce with the nearby town of Novoli, serving local passenger and commuter traffic in the Apulia region.

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_69f76e6f10008190aea41746aa1b186e completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f7c7a1039c81909c83c12714d86cb0 completed May 3, 2026, 10:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3c171db5c08190aa17c3ede32bd4be completed June 24, 2026, 5:42 p.m.
NEDg Description generation batch_6a3c193a1fc881908332ab00462372e1 completed June 24, 2026, 5:51 p.m.
NED2 Entity disambiguation (via description) batch_6a3c57b8bd4c81909d429a799dac9063 completed June 24, 2026, 10:18 p.m.
Created at: May 3, 2026, 4:12 p.m.