Triple

T36499124
Position Surface form Disambiguated ID Type / Status
Subject Sungai Batu E899273 entity
Predicate nearbySettlement P350 FINISHED
Object Batu Caves town
Batu Caves town is a suburban area in the Gombak District of Selangor, Malaysia, best known as the gateway to the famous Batu Caves Hindu temple complex and limestone hill.
E2185396 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: Batu Caves town | Statement: [Sungai Batu, nearbySettlement, Batu Caves town]
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: Batu Caves town
Triple: [Sungai Batu, nearbySettlement, Batu Caves town]
Generated description
Batu Caves town is a suburban area in the Gombak District of Selangor, Malaysia, best known as the gateway to the famous Batu Caves Hindu temple complex and limestone hill.

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_69f76e5b92088190933afda3f7531dd4 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7c1c1e4d88190bce8e5a4ef6dcc8d completed May 3, 2026, 9:44 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39cfe8066c81908719e91c02cb1aaf completed June 23, 2026, 12:14 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.