Triple

T37129976
Position Surface form Disambiguated ID Type / Status
Subject Southern Islands of Singapore E919490 entity
Predicate hasPart P35 FINISHED
Object Pulau Ular
Pulau Ular is a small, largely undeveloped island within Singapore’s Southern Islands, known primarily for its restricted access and use for industrial or utility-related purposes rather than tourism.
 E2215087 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: Pulau Ular | Statement: [Southern Islands of Singapore, hasPart, Pulau Ular]
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: Pulau Ular
Triple: [Southern Islands of Singapore, hasPart, Pulau Ular]
Generated description
Pulau Ular is a small, largely undeveloped island within Singapore’s Southern Islands, known primarily for its restricted access and use for industrial or utility-related purposes rather than tourism.

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_69f76e9d13e48190a108f7fbf80ff375 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fb303d25408190bfa0d55a25251c2c completed May 6, 2026, 12:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a402ba5965481909ef4d9b4e7608192 completed June 27, 2026, 7:59 p.m.
NEDg Description generation batch_6a402c0e80cc819084457297be9a3d02 completed June 27, 2026, 8:01 p.m.
NED2 Entity disambiguation (via description) batch_6a402cf925948190bcb4e1f3848eef85 completed June 27, 2026, 8:05 p.m.
Created at: May 3, 2026, 4:15 p.m.