Triple

T24680097
Position Surface form Disambiguated ID Type / Status
Subject Tenement Museum E611104 entity
Predicate foundedBy P104 FINISHED
Object Ruth J. Abram
Ruth J. Abram is a public historian and social activist best known as the founder of New York City's Tenement Museum, which preserves and interprets the immigrant experience on the Lower East Side.
 E1677899 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: Ruth J. Abram | Statement: [Tenement Museum, foundedBy, Ruth J. Abram]
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: Ruth J. Abram
Triple: [Tenement Museum, foundedBy, Ruth J. Abram]
Generated description
Ruth J. Abram is a public historian and social activist best known as the founder of New York City's Tenement Museum, which preserves and interprets the immigrant experience on the Lower East Side.

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_69e2c4d5c2dc8190ac857dea25ec6ce9 completed April 17, 2026, 11:40 p.m.
NER Named-entity recognition batch_69f40fbf68d48190b92e809a8947d60e completed May 1, 2026, 2:28 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10895186348190b4865ab15bb5e1e9 completed May 22, 2026, 4:50 p.m.
NEDg Description generation batch_6a1089ee13c08190938666df6ba526e8 completed May 22, 2026, 4:53 p.m.
NED2 Entity disambiguation (via description) batch_6a108a6eeda48190a9a132ea2804d41c completed May 22, 2026, 4:55 p.m.
Created at: April 18, 2026, 3:08 a.m.