Triple

T29164535
Position Surface form Disambiguated ID Type / Status
Subject The Next Accident E739277 entity
Predicate follows P134 FINISHED
Object The Third Victim
The Third Victim is a crime thriller novel by Lisa Gardner featuring FBI profiler Pierce Quincy, centered on a deadly school shooting and a complex investigation into the truth behind it.
 E739276 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 Third Victim | Statement: [The Next Accident, follows, The Third Victim]
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 Third Victim
Triple: [The Next Accident, follows, The Third Victim]
Generated description
The Third Victim is a crime thriller novel by Lisa Gardner featuring FBI profiler Pierce Quincy, centered on a deadly school shooting and a complex investigation into the truth behind it.

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_69f07cb528fc8190a556b73990c347c8 completed April 28, 2026, 9:24 a.m.
NER Named-entity recognition batch_69f662d421f88190ac5e65ae10e5c2b7 completed May 2, 2026, 8:47 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2569b044e48190baf5824656cf9e65 completed June 7, 2026, 12:53 p.m.
NEDg Description generation batch_6a256bb169608190abd77de9028f3e6a completed June 7, 2026, 1:01 p.m.
NED2 Entity disambiguation (via description) batch_6a256c0c4e8881908ca552487555935f completed June 7, 2026, 1:03 p.m.
Created at: April 28, 2026, 11:49 a.m.