Triple

T12001024
Position Surface form Disambiguated ID Type / Status
Subject David Denman E285659 entity
Predicate notableWork P4 FINISHED
Object ER
ER is a long-running American medical drama television series set in a Chicago hospital’s emergency room, known for its fast-paced storytelling and ensemble cast.
 E82125 NE FINISHED

How this triple was built (4 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: ER | Statement: [David Denman, notableWork, ER]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ER
Context triple: [David Denman, notableWork, ER]
  • A. ER
    ER is the zone code for Eastern Railway, one of the major railway zones of Indian Railways headquartered in Kolkata.
  • B. ER
    ER is the abbreviation used to designate the Eastern Region of British Rail, a major administrative division of the former British railway network covering eastern England.
  • C. ER
    ER is the IATA airline designator assigned to SereneAir, a Pakistani low-cost carrier.
  • D. ER
    ER is the vehicle registration code assigned to the German city of Erlangen in the state of Bavaria.
  • E. ER
    ER is the commonly used abbreviation for United Russia, the dominant ruling political party in the Russian Federation.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: ER
Triple: [David Denman, notableWork, ER]
Generated description
ER is a long-running American medical drama television series set in a Chicago hospital’s emergency room, known for its fast-paced storytelling and ensemble cast.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ER
Target entity description: ER is a long-running American medical drama television series set in a Chicago hospital’s emergency room, known for its fast-paced storytelling and ensemble cast.
  • A. ER chosen
    ER is a critically acclaimed American medical drama television series that follows the personal and professional lives of staff in a busy Chicago emergency room.
  • B. ER
    ER is the commonly used abbreviation for United Russia, the dominant ruling political party in the Russian Federation.
  • C. ER
    ER is the abbreviation used to designate the Eastern Region of British Rail, a major administrative division of the former British railway network covering eastern England.
  • D. ER
    ER is the vehicle registration code assigned to the German city of Erlangen in the state of Bavaria.
  • E. ER
    ER is the zone code for Eastern Railway, one of the major railway zones of Indian Railways headquartered in Kolkata.
  • F. None of above.

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_69d6ab44a77c8190a652f4b27164e4ef completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903c36b248190b446b17def94885b completed April 10, 2026, 2:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69f4729eb4a081909d93b3fc74509d86 completed May 1, 2026, 9:30 a.m.
NEDg Description generation batch_69f47b7e4a40819085680c48eed5418a completed May 1, 2026, 10:07 a.m.
NED2 Entity disambiguation (via description) batch_69f47df40a8c8190bd7350ba27f57214 completed May 1, 2026, 10:18 a.m.
Created at: April 8, 2026, 9:46 p.m.