Triple

T26728518
Position Surface form Disambiguated ID Type / Status
Subject Val d’Europe RER station E673899 entity
Predicate servedByBranch P13713 FINISHED
Object A4 branch of RER A
The A4 branch of RER A is a suburban rail line segment of the Paris RER network that runs eastward from central Paris toward Marne-la-Vallée, serving stations such as Val d’Europe.
 E1736757 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: A4 branch of RER A | Statement: [Val d’Europe RER station, servedByBranch, A4 branch of RER A]
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: A4 branch of RER A
Triple: [Val d’Europe RER station, servedByBranch, A4 branch of RER A]
Generated description
The A4 branch of RER A is a suburban rail line segment of the Paris RER network that runs eastward from central Paris toward Marne-la-Vallée, serving stations such as Val d’Europe.

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_69eecda481d08190aea69f2f7c745f56 completed April 27, 2026, 2:44 a.m.
NER Named-entity recognition batch_69f618401d2481908b10199b30333192 completed May 2, 2026, 3:29 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11fe963c8c8190a99967b3cf543586 completed May 23, 2026, 7:23 p.m.
NEDg Description generation batch_6a11ff16d2608190b5f74ef1b9532b93 completed May 23, 2026, 7:25 p.m.
NED2 Entity disambiguation (via description) batch_6a11ff824b2881909354c7ab3749ccaf completed May 23, 2026, 7:26 p.m.
Created at: April 27, 2026, 3:43 a.m.