Triple

T33461281
Position Surface form Disambiguated ID Type / Status
Subject Trapper John McIntyre E856921 entity
Predicate fictionalUniverse P3758 FINISHED
Object M*A*S*H franchise
The M*A*S*H franchise is a comedy-drama media series spanning novels, a film, and a long-running television show that follows the lives of staff at a mobile army surgical hospital during the Korean War.
 E2063438 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: M*A*S*H franchise | Statement: [Trapper John McIntyre, fictionalUniverse, M*A*S*H franchise]
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: M*A*S*H franchise
Triple: [Trapper John McIntyre, fictionalUniverse, M*A*S*H franchise]
Generated description
The M*A*S*H franchise is a comedy-drama media series spanning novels, a film, and a long-running television show that follows the lives of staff at a mobile army surgical hospital during the Korean War.

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_69f34973461481909c701c98ebd75623 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6e4d3a60881908d5bbe01c55f796b completed May 3, 2026, 6:01 a.m.
NED1 Entity disambiguation (via context triple) batch_6a363c79991881909148927e62ef2bca completed June 20, 2026, 7:08 a.m.
NEDg Description generation batch_6a364657ccc08190bde7228169c451df completed June 20, 2026, 7:50 a.m.
NED2 Entity disambiguation (via description) batch_6a3646ad8cf4819093ecf58402b4e675 completed June 20, 2026, 7:52 a.m.
Created at: May 1, 2026, 1:37 a.m.