Triple

T28411512
Position Surface form Disambiguated ID Type / Status
Subject Andrew Gross E719675 entity
Predicate notableWork P4 FINISHED
Object The Lost Women of Paris
The Lost Women of Paris is a historical thriller novel by Andrew Gross that follows the stories of female secret agents in World War II and the aftermath of their dangerous missions.
 E1816940 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: The Lost Women of Paris | Statement: [Andrew Gross, notableWork, The Lost Women of Paris]
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: The Lost Women of Paris
Triple: [Andrew Gross, notableWork, The Lost Women of Paris]
Generated description
The Lost Women of Paris is a historical thriller novel by Andrew Gross that follows the stories of female secret agents in World War II and the aftermath of their dangerous missions.

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_69eff6f0f37c8190b37bc6fab08a9449 completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64dbc9be4819082633afaeb8136dc completed May 2, 2026, 7:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1633164af08190afff502873b1e54b completed May 26, 2026, 11:56 p.m.
NEDg Description generation batch_6a1633dd88848190bf73982c2ce00ffd completed May 26, 2026, 11:59 p.m.
NED2 Entity disambiguation (via description) batch_6a16365587e08190b93663807e950946 completed May 27, 2026, 12:09 a.m.
Created at: April 28, 2026, 1:27 a.m.