Triple

T28344196
Position Surface form Disambiguated ID Type / Status
Subject Jody Shields E717901 entity
Predicate notableWork P4 FINISHED
Object The Fig Eater
The Fig Eater is a historical mystery novel set in early 20th-century Vienna that intertwines forensic investigation with psychological and cultural intrigue.
 E1814101 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 Fig Eater | Statement: [Jody Shields, notableWork, The Fig Eater]
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 Fig Eater
Triple: [Jody Shields, notableWork, The Fig Eater]
Generated description
The Fig Eater is a historical mystery novel set in early 20th-century Vienna that intertwines forensic investigation with psychological and cultural intrigue.

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_69eff6eb30388190b898b96c4be6f49d completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64c05755c8190a1295178ec9a7f2d completed May 2, 2026, 7:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1627c10170819088118c3965925c92 completed May 26, 2026, 11:07 p.m.
NEDg Description generation batch_6a16290c6a808190817f7bee27d4e0ee completed May 26, 2026, 11:13 p.m.
NED2 Entity disambiguation (via description) batch_6a162a28a0bc81909d87cabc75fdb1c3 completed May 26, 2026, 11:18 p.m.
Created at: April 28, 2026, 12:41 a.m.