Triple

T36441245
Position Surface form Disambiguated ID Type / Status
Subject Domont station E897733 entity
Predicate locatedOnRailwayLine P848 FINISHED
Object Paris–Luzarches railway
The Paris–Luzarches railway is a suburban rail line in the Île-de-France region of France that connects Paris to the town of Luzarches, serving various commuter stations along its route.
 E2185092 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: Paris–Luzarches railway | Statement: [Domont station, locatedOnRailwayLine, Paris–Luzarches 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: Paris–Luzarches railway
Triple: [Domont station, locatedOnRailwayLine, Paris–Luzarches railway]
Generated description
The Paris–Luzarches railway is a suburban rail line in the Île-de-France region of France that connects Paris to the town of Luzarches, serving various commuter stations along its route.

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_69f76e5720b481908f8177ac24a7560b completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7bd6dfa9c819087468d181165d980 completed May 3, 2026, 9:26 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39cfc6f34c8190ab4bf08fcb988e43 completed June 23, 2026, 12:13 a.m.
NEDg Description generation batch_6a39d06ec8448190bce52dd9dcb925f4 completed June 23, 2026, 12:16 a.m.
NED2 Entity disambiguation (via description) batch_6a39d12766ac8190a505dd6d49293937 completed June 23, 2026, 12:19 a.m.
Created at: May 3, 2026, 4:10 p.m.