Triple

T30798759
Position Surface form Disambiguated ID Type / Status
Subject Ackerman E784304 entity
Predicate hasNotableBearer P458 FINISHED
Object Debra Ackerman
Debra Ackerman is a fictional character from the television series "Gossip Girl," known as the mother of Chuck Bass.
E1984425 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: Debra Ackerman | Statement: [Ackerman, hasNotableBearer, Debra Ackerman]
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: Debra Ackerman
Triple: [Ackerman, hasNotableBearer, Debra Ackerman]
Generated description
Debra Ackerman is a fictional character from the television series "Gossip Girl," known as the mother of Chuck Bass.

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_69f224b2e2a48190b19aa43db9da5b67 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f690380ad8819093157b52b303beca completed May 3, 2026, midnight
NED1 Entity disambiguation (via context triple) batch_6a2e8a0de6908190b501c238295db758 completed June 14, 2026, 11:01 a.m.
NEDg Description generation batch_6a2e8aba8d9481908df3439168b0f62f completed June 14, 2026, 11:04 a.m.
NED2 Entity disambiguation (via description) batch_6a2e8b6f6f2c819098e1787c61964edd completed June 14, 2026, 11:07 a.m.
Created at: April 29, 2026, 8:42 p.m.