Triple

T25208458
Position Surface form Disambiguated ID Type / Status
Subject Woollahra Municipal Council E631618 entity
Predicate administers P123 FINISHED
Object Point Piper Peninsula
Point Piper Peninsula is an affluent harbourside residential area in Sydney, Australia, known for its luxury waterfront properties and some of the country’s most expensive real estate.
E1666366 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: Point Piper Peninsula | Statement: [Woollahra Municipal Council, administers, Point Piper Peninsula]
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: Point Piper Peninsula
Triple: [Woollahra Municipal Council, administers, Point Piper Peninsula]
Generated description
Point Piper Peninsula is an affluent harbourside residential area in Sydney, Australia, known for its luxury waterfront properties and some of the country’s most expensive real estate.

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_69e75a8d1aa48190a4320acd3654762c completed April 21, 2026, 11:07 a.m.
NER Named-entity recognition batch_69f474bda9288190947d325fe514571d completed May 1, 2026, 9:39 a.m.
NED1 Entity disambiguation (via context triple) batch_6a105d3094b08190b69af586b21f9ee6 completed May 22, 2026, 1:42 p.m.
NEDg Description generation batch_6a105e53f9bc8190a4b0929a68d83b0a completed May 22, 2026, 1:47 p.m.
NED2 Entity disambiguation (via description) batch_6a105ed8d78c81908eb3648c65de38b1 completed May 22, 2026, 1:49 p.m.
Created at: April 21, 2026, 12:57 p.m.