Triple

T29562462
Position Surface form Disambiguated ID Type / Status
Subject Doug MacRay E750073 entity
Predicate settingNeighborhood P988 FINISHED
Object Charlestown
Charlestown is a historic Boston neighborhood known for its tight-knit, working-class community and its depiction as a hub of bank robbers in the film "The Town," whose main character is Doug MacRay.
E708192 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: Charlestown | Statement: [Doug MacRay, settingNeighborhood, Charlestown]
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: Charlestown
Triple: [Doug MacRay, settingNeighborhood, Charlestown]
Generated description
Charlestown is a historic Boston neighborhood known for its tight-knit, working-class community and its depiction as a hub of bank robbers in the film "The Town," whose main character is Doug MacRay.

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_69f0bd4919e48190942b2a13d5b97d03 completed April 28, 2026, 1:59 p.m.
NER Named-entity recognition batch_69f66d1e64e8819080579603e5bcdeb7 completed May 2, 2026, 9:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26615a118c81908fb39ebe71df478c completed June 8, 2026, 6:29 a.m.
NEDg Description generation batch_6a26658b86e88190b68b3a7d183a72e9 completed June 8, 2026, 6:47 a.m.
NED2 Entity disambiguation (via description) batch_6a266c326a7081909d55ff20b5c3b851 completed June 8, 2026, 7:16 a.m.
Created at: April 28, 2026, 5:21 p.m.