Triple

T31266546
Position Surface form Disambiguated ID Type / Status
Subject Early Autumn E797267 entity
Predicate hasCharacterRole P12208 FINISHED
Object Hawk is Spenser’s ally and enforcer
Hawk is a formidable, streetwise ally and enforcer who assists the private investigator Spenser in the novel "Early Autumn."
E1953293 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: Hawk is Spenser’s ally and enforcer | Statement: [Early Autumn, hasCharacterRole, Hawk is Spenser’s ally and enforcer]
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: Hawk is Spenser’s ally and enforcer
Triple: [Early Autumn, hasCharacterRole, Hawk is Spenser’s ally and enforcer]
Generated description
Hawk is a formidable, streetwise ally and enforcer who assists the private investigator Spenser in the novel "Early Autumn."

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_69f224de2bbc819081af6c32e1d857b9 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69d9243b48190b665977bd4729392 completed May 3, 2026, 12:57 a.m.
NED1 Entity disambiguation (via context triple) batch_6a296bffdb688190bbf7988202c50a04 completed June 10, 2026, 1:52 p.m.
NEDg Description generation batch_6a296c9ac13c819081c06411d80071c1 completed June 10, 2026, 1:54 p.m.
NED2 Entity disambiguation (via description) batch_6a299c9b54dc8190abd715cd88979541 completed June 10, 2026, 5:19 p.m.
Created at: April 29, 2026, 9:13 p.m.