Triple

T37204949
Position Surface form Disambiguated ID Type / Status
Subject Lieutenant Ray Calletano E922141 entity
Predicate worksUnder P3062 FINISHED
Object Chief Earl Eischied
Chief Earl Eischied is the tough, streetwise New York City police chief and title character of the late-1970s American crime drama TV series "Eischied."
E2217691 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: Chief Earl Eischied | Statement: [Lieutenant Ray Calletano, worksUnder, Chief Earl Eischied]
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: Chief Earl Eischied
Triple: [Lieutenant Ray Calletano, worksUnder, Chief Earl Eischied]
Generated description
Chief Earl Eischied is the tough, streetwise New York City police chief and title character of the late-1970s American crime drama TV series "Eischied."

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_69f76ea4849481909b4a3073efb0114c completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fb36478fcc8190b1e71b5c543d2ba5 completed May 6, 2026, 12:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40361f7efc8190b2a4132ea44352f0 completed June 27, 2026, 8:44 p.m.
NEDg Description generation batch_6a403755f6b08190ac5625cb3282fe51 completed June 27, 2026, 8:49 p.m.
NED2 Entity disambiguation (via description) batch_6a403874ce488190b8f53ed77feb46af completed June 27, 2026, 8:54 p.m.
Created at: May 3, 2026, 4:15 p.m.