Triple

T27306449
Position Surface form Disambiguated ID Type / Status
Subject Scott Heiferman E689071 entity
Predicate associatedWith P37 FINISHED
Object New York tech community
The New York tech community is a vibrant ecosystem of startups, investors, technologists, and entrepreneurs centered in New York City, known for its diversity, cross-industry innovation, and strong meetup and networking culture.
E1766735 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: New York tech community | Statement: [Scott Heiferman, associatedWith, New York tech community]
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: New York tech community
Triple: [Scott Heiferman, associatedWith, New York tech community]
Generated description
The New York tech community is a vibrant ecosystem of startups, investors, technologists, and entrepreneurs centered in New York City, known for its diversity, cross-industry innovation, and strong meetup and networking culture.

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_69ef355b931c8190a63cafaf7bcc008b completed April 27, 2026, 10:07 a.m.
NER Named-entity recognition batch_69f627afaf648190b187cafdc6f3e8ce completed May 2, 2026, 4:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a129caf72dc81909d4bad8cd650f796 completed May 24, 2026, 6:37 a.m.
NEDg Description generation batch_6a129e71ddc481908a3659f89b0beb0f completed May 24, 2026, 6:45 a.m.
NED2 Entity disambiguation (via description) batch_6a129fa2db9c8190be46762dee9a6394 completed May 24, 2026, 6:50 a.m.
Created at: April 27, 2026, 11:25 a.m.