Triple

T26111350
Position Surface form Disambiguated ID Type / Status
Subject Marilyn Monroe Towers E658704 entity
Predicate refersTo P37 FINISHED
Object Absolute World Tower 1
Absolute World Tower 1 is a curvaceous, twisting residential skyscraper in Mississauga, Ontario, famed for its distinctive "Marilyn Monroe" silhouette.
E1709691 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: Absolute World Tower 1 | Statement: [Marilyn Monroe Towers, refersTo, Absolute World Tower 1]
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: Absolute World Tower 1
Triple: [Marilyn Monroe Towers, refersTo, Absolute World Tower 1]
Generated description
Absolute World Tower 1 is a curvaceous, twisting residential skyscraper in Mississauga, Ontario, famed for its distinctive "Marilyn Monroe" silhouette.

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_69ee5bc20298819099a42be042eb2349 completed April 26, 2026, 6:38 p.m.
NER Named-entity recognition batch_69f6077c1be881909731824864babeb3 completed May 2, 2026, 2:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1127508e888190918ebd1225b80466 completed May 23, 2026, 4:04 a.m.
NEDg Description generation batch_6a1135205bdc81908dc42970c5f9b60d completed May 23, 2026, 5:03 a.m.
NED2 Entity disambiguation (via description) batch_6a113610d1d8819097ce5070e47a7645 completed May 23, 2026, 5:07 a.m.
Created at: April 26, 2026, 8:02 p.m.