Triple

T28437390
Position Surface form Disambiguated ID Type / Status
Subject Durst Organization E715303 entity
Predicate knownFor P22 FINISHED
Object Times Square office towers
Times Square office towers are prominent high-rise commercial buildings in New York City’s Times Square, housing major corporate offices and illuminated advertising displays in one of the world’s busiest urban centers.
 E174004 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: Times Square office towers | Statement: [Durst Organization, knownFor, Times Square office towers]
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: Times Square office towers
Triple: [Durst Organization, knownFor, Times Square office towers]
Generated description
Times Square office towers are prominent high-rise commercial buildings in New York City’s Times Square, housing major corporate offices and illuminated advertising displays in one of the world’s busiest urban centers.

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_69efd6b253888190b3c7222ed6a403a8 completed April 27, 2026, 9:35 p.m.
NER Named-entity recognition batch_69f64e3b35fc819096c20346642c2a38 completed May 2, 2026, 7:19 p.m.
NED1 Entity disambiguation (via context triple) batch_6a16332b307c8190846a2ec13d4ac46c completed May 26, 2026, 11:56 p.m.
NEDg Description generation batch_6a1634e773588190b0eda7b51b029b11 completed May 27, 2026, 12:03 a.m.
NED2 Entity disambiguation (via description) batch_6a1635789f2c8190ade17d4f34d65def completed May 27, 2026, 12:06 a.m.
Created at: April 28, 2026, 1:43 a.m.