Triple

T27571469
Position Surface form Disambiguated ID Type / Status
Subject Resort Gateway Station E696044 entity
Predicate connectsTo P845 FINISHED
Object JR East Maihama Station
JR East Maihama Station is a major railway station in Urayasu, Chiba Prefecture, serving as the primary JR access point for Tokyo Disney Resort.
E1826850 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: JR East Maihama Station | Statement: [Resort Gateway Station, connectsTo, JR East Maihama Station]
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: JR East Maihama Station
Triple: [Resort Gateway Station, connectsTo, JR East Maihama Station]
Generated description
JR East Maihama Station is a major railway station in Urayasu, Chiba Prefecture, serving as the primary JR access point for Tokyo Disney Resort.

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_69ef53891af88190a193c5e2a1dac9b1 completed April 27, 2026, 12:16 p.m.
NER Named-entity recognition batch_69f62fec821081909ae17c4c3bdcae19 completed May 2, 2026, 5:10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1cc3528aec81909fd3cdf2ad74953e completed May 31, 2026, 11:25 p.m.
NEDg Description generation batch_6a1cc3c360808190a2961b3e0a3c839f completed May 31, 2026, 11:26 p.m.
NED2 Entity disambiguation (via description) batch_6a1cc45223488190a914244c6245a86f completed May 31, 2026, 11:29 p.m.
Created at: April 27, 2026, 1:43 p.m.