Triple

T23835948
Position Surface form Disambiguated ID Type / Status
Subject Jakarta MRT E590852 entity
Predicate partOf P40 FINISHED
Object Jakarta public transportation network
The Jakarta public transportation network is an integrated urban transit system in Indonesia’s capital that combines metro rail, commuter trains, buses, and other modes to serve millions of daily passengers.
E1604050 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: Jakarta public transportation network | Statement: [Jakarta MRT, partOf, Jakarta public transportation network]
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: Jakarta public transportation network
Triple: [Jakarta MRT, partOf, Jakarta public transportation network]
Generated description
The Jakarta public transportation network is an integrated urban transit system in Indonesia’s capital that combines metro rail, commuter trains, buses, and other modes to serve millions of daily passengers.

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_69e25d1de32c8190a907afe9c3d6cd6d completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f1c882f9148190bb28fe7566ef1e70 completed April 29, 2026, 8:59 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f69a372108190993dc497e8eaf81b completed May 21, 2026, 8:22 p.m.
NEDg Description generation batch_6a0f6d4007308190b2d474963d0a9b8c completed May 21, 2026, 8:38 p.m.
NED2 Entity disambiguation (via description) batch_6a0f6e02703881908fa9c327c5808bf5 completed May 21, 2026, 8:41 p.m.
Created at: April 17, 2026, 8:07 p.m.