Triple

T37374041
Position Surface form Disambiguated ID Type / Status
Subject Anita Page E927922 entity
Predicate appearedIn P795 FINISHED
Object War Nurse
War Nurse is a 1930 American pre-Code drama film set during World War I that follows a group of volunteer nurses facing the emotional and physical toll of serving near the front lines.
 E2224352 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: War Nurse | Statement: [Anita Page, appearedIn, War Nurse]
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: War Nurse
Triple: [Anita Page, appearedIn, War Nurse]
Generated description
War Nurse is a 1930 American pre-Code drama film set during World War I that follows a group of volunteer nurses facing the emotional and physical toll of serving near the front lines.

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_69f76eb820248190a5c395ca50ad002a completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fb8d103fe881908966c684f0415986 completed May 6, 2026, 6:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a406cf124b8819095f5d4434cf739a5 completed June 28, 2026, 12:38 a.m.
NEDg Description generation batch_6a406e1111c08190af357e4e318772ba completed June 28, 2026, 12:42 a.m.
NED2 Entity disambiguation (via description) batch_6a406ed77a5c819091554d7e4561aa0b completed June 28, 2026, 12:46 a.m.
Created at: May 3, 2026, 4:16 p.m.