Triple

T26609120
Position Surface form Disambiguated ID Type / Status
Subject C. Gardner Sullivan E667861 entity
Predicate notableWork P4 FINISHED
Object The Crowded Hour
The Crowded Hour is a 1925 American silent war drama film, adapted from a popular stage play, that follows the experiences of a society woman turned World War I nurse.
 E1732148 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: The Crowded Hour | Statement: [C. Gardner Sullivan, notableWork, The Crowded Hour]
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: The Crowded Hour
Triple: [C. Gardner Sullivan, notableWork, The Crowded Hour]
Generated description
The Crowded Hour is a 1925 American silent war drama film, adapted from a popular stage play, that follows the experiences of a society woman turned World War I nurse.

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_69ee9cfd20348190bb1255d2603efb7a completed April 26, 2026, 11:17 p.m.
NER Named-entity recognition batch_69f61576b3f48190bc87dcdf685b4532 completed May 2, 2026, 3:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11c84571f88190819d26ab44f051ee completed May 23, 2026, 3:31 p.m.
NEDg Description generation batch_6a11c990b5b0819089db74aa73b886a0 completed May 23, 2026, 3:36 p.m.
NED2 Entity disambiguation (via description) batch_6a11ca6f162c8190a8c7fbc1e188ea90 completed May 23, 2026, 3:40 p.m.
Created at: April 27, 2026, 2:16 a.m.