Triple

T24043081
Position Surface form Disambiguated ID Type / Status
Subject Letters from Hiding (largely lost) E595438 entity
Predicate associatedWith P37 FINISHED
Object Pfeffer family
The Pfeffer family was a Jewish family connected to the circle of people in hiding with Anne Frank during the Nazi occupation, notably including dentist Fritz Pfeffer.
E1612390 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: Pfeffer family | Statement: [Letters from Hiding (largely lost), associatedWith, Pfeffer family]
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: Pfeffer family
Triple: [Letters from Hiding (largely lost), associatedWith, Pfeffer family]
Generated description
The Pfeffer family was a Jewish family connected to the circle of people in hiding with Anne Frank during the Nazi occupation, notably including dentist Fritz Pfeffer.

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_69e288c06a908190899cad4531f32c9a completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1d8db3b4c81908a36eace8ec136cc completed April 29, 2026, 10:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f7eb60d8481908416940439a0b3ba completed May 21, 2026, 9:52 p.m.
NEDg Description generation batch_6a0f7f4ef5e88190b53cdf7135b28cac completed May 21, 2026, 9:55 p.m.
NED2 Entity disambiguation (via description) batch_6a0f7fe7f7248190a377212661dd56b1 completed May 21, 2026, 9:58 p.m.
Created at: April 17, 2026, 9:58 p.m.