Triple

T21243037
Position Surface form Disambiguated ID Type / Status
Subject Thomas Del Ruth E523525 entity
Predicate workedOn P3 FINISHED
Object ER
ER is a critically acclaimed American medical drama television series that follows the personal and professional lives of staff in a busy Chicago hospital emergency room.
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: [Thomas Del Ruth, workedOn, ER]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ER
Context triple: [Thomas Del Ruth, workedOn, 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 two-letter ISO 3166-1 alpha-2 country code for Eritrea, a nation in the Horn of Africa.
  • D. ER
    ER is the ISO 3166-1 alpha-2 country code for Eritrea, a nation in the Horn of Africa.
  • E. ER
    ER is the station code for Ermita station, a stop on Manila’s Light Rail Transit system in the Philippines.
  • 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: [Thomas Del Ruth, workedOn, ER]
Generated description
ER is a critically acclaimed American medical drama television series that follows the personal and professional lives of staff in a busy Chicago hospital emergency room.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: ER
Target entity description: ER is a critically acclaimed American medical drama television series that follows the personal and professional lives of staff in a busy Chicago hospital emergency room.
  • 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 standard abbreviation used for the Erie Otters, a junior ice hockey team in the Ontario Hockey League.
  • C. ER
    ER is the commonly used abbreviation for United Russia, the dominant ruling political party in the Russian Federation.
  • D. ER
    ER is the station code for Ermita station, a stop on Manila’s Light Rail Transit system in the Philippines.
  • E. 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.
  • 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_69e0b513b89c81908b27147e91368db2 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e7352507448190ba1f14cef16d69be completed April 21, 2026, 8:28 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0986f705808190ba3a85d2dacb8818 completed May 17, 2026, 9:14 a.m.
NEDg Description generation batch_6a0988086a648190856057c1327a6ab1 completed May 17, 2026, 9:19 a.m.
NED2 Entity disambiguation (via description) batch_6a0988d4ada081909fe532955b204b94 completed May 17, 2026, 9:22 a.m.
Created at: April 16, 2026, 3:47 p.m.