Triple

T33773949
Position Surface form Disambiguated ID Type / Status
Subject The Candidate (Lost) E865460 entity
Predicate featuresCharacter P626 FINISHED
Object Man in Black
The Man in Black is a mysterious, shape-shifting antagonist in the television series Lost, embodying the island’s dark, malevolent force and serving as Jacob’s primary adversary.
E2068352 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: Man in Black | Statement: [The Candidate (Lost), featuresCharacter, Man in Black]
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: Man in Black
Triple: [The Candidate (Lost), featuresCharacter, Man in Black]
Generated description
The Man in Black is a mysterious, shape-shifting antagonist in the television series Lost, embodying the island’s dark, malevolent force and serving as Jacob’s primary adversary.

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_69f3498df6f88190bf9647ea4e4a956e completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6fc945d788190baad1a0a9da57bed completed May 3, 2026, 7:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3665839e448190b0f7b0134abf6605 completed June 20, 2026, 10:03 a.m.
NEDg Description generation batch_6a3666f6c3588190bee946d4f78efbd0 completed June 20, 2026, 10:09 a.m.
NED2 Entity disambiguation (via description) batch_6a366787e30081909399883578eb65bd completed June 20, 2026, 10:12 a.m.
Created at: May 1, 2026, 1:45 a.m.