Triple

T28821094
Position Surface form Disambiguated ID Type / Status
Subject Tom McLoughlin E727764 entity
Predicate directed P7373 FINISHED
Object Murder in Greenwich
Murder in Greenwich is a 2002 television crime drama film that dramatizes the real-life 1975 murder of Martha Moxley and the subsequent investigation involving the Kennedy family.
E1835476 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: Murder in Greenwich | Statement: [Tom McLoughlin, directed, Murder in Greenwich]
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: Murder in Greenwich
Triple: [Tom McLoughlin, directed, Murder in Greenwich]
Generated description
Murder in Greenwich is a 2002 television crime drama film that dramatizes the real-life 1975 murder of Martha Moxley and the subsequent investigation involving the Kennedy family.

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_69f0319d09088190bbf14cdf1987792a completed April 28, 2026, 4:03 a.m.
NER Named-entity recognition batch_69f658f7159c8190a9e3d4e60112ad53 completed May 2, 2026, 8:05 p.m.
NED1 Entity disambiguation (via context triple) batch_6a24bba4eff88190bb68dd4d9efddba5 completed June 7, 2026, 12:30 a.m.
NEDg Description generation batch_6a24bfc8d5f48190897d403ba203f298 completed June 7, 2026, 12:48 a.m.
NED2 Entity disambiguation (via description) batch_6a24c3ee6bdc8190a0bbf5cb4503d57a completed June 7, 2026, 1:05 a.m.
Created at: April 28, 2026, 6:34 a.m.