Triple

T29319365
Position Surface form Disambiguated ID Type / Status
Subject Lau Kin-ming E743471 entity
Predicate adaptedAs P1926 FINISHED
Object Colin Sullivan in The Departed
Colin Sullivan in The Departed is a corrupt Massachusetts State Police officer secretly working as a mole for Irish mob boss Frank Costello in Martin Scorsese’s crime thriller.
E1835908 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: Colin Sullivan in The Departed | Statement: [Lau Kin-ming, adaptedAs, Colin Sullivan in The Departed]
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: Colin Sullivan in The Departed
Triple: [Lau Kin-ming, adaptedAs, Colin Sullivan in The Departed]
Generated description
Colin Sullivan in The Departed is a corrupt Massachusetts State Police officer secretly working as a mole for Irish mob boss Frank Costello in Martin Scorsese’s crime thriller.

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_69f0912502c8819087d9e8398ee991a8 completed April 28, 2026, 10:51 a.m.
NER Named-entity recognition batch_69f665ef0d388190a0d3a2169e6254f2 completed May 2, 2026, 9 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25a8702f388190b3e7d79112ab3000 completed June 7, 2026, 5:20 p.m.
NEDg Description generation batch_6a25aca216088190b6e106c9172f638c completed June 7, 2026, 5:38 p.m.
NED2 Entity disambiguation (via description) batch_6a25b13b60088190bfe08fd65547a593 completed June 7, 2026, 5:58 p.m.
Created at: April 28, 2026, 1:22 p.m.