Triple

T26648974
Position Surface form Disambiguated ID Type / Status
Subject North South Line E668996 entity
Predicate connectsResidentialArea P96376 FINISHED
Object Ang Mo Kio
Ang Mo Kio is a major residential town and planning area in Singapore known for its public housing estates, amenities, and connectivity via the MRT network.
 E1767579 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: Ang Mo Kio | Statement: [North South Line, connectsResidentialArea, Ang Mo Kio]
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: Ang Mo Kio
Triple: [North South Line, connectsResidentialArea, Ang Mo Kio]
Generated description
Ang Mo Kio is a major residential town and planning area in Singapore known for its public housing estates, amenities, and connectivity via the MRT network.

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_69ee9d00eb5481908d6c6d0ada2f0c9a completed April 26, 2026, 11:17 p.m.
NER Named-entity recognition batch_69f6167875e8819083782aec7b793143 completed May 2, 2026, 3:21 p.m.
NED1 Entity disambiguation (via context triple) batch_6a129c7e478081908b88b481d4227752 completed May 24, 2026, 6:36 a.m.
NEDg Description generation batch_6a129dc563e081909b6e07e29aad6ddb completed May 24, 2026, 6:42 a.m.
NED2 Entity disambiguation (via description) batch_6a129e5f7e348190af4a279de8ef8caa completed May 24, 2026, 6:44 a.m.
Created at: April 27, 2026, 2:32 a.m.