Triple

T31221703
Position Surface form Disambiguated ID Type / Status
Subject Ryan J. Condal E796024 entity
Predicate wrote P2831 FINISHED
Object Die Hard Year One (unproduced script)
Die Hard Year One (unproduced script) is an unmade prequel screenplay in the Die Hard franchise that explores a younger John McClane in his early years as a New York City cop.
 E1951616 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: Die Hard Year One (unproduced script) | Statement: [Ryan J. Condal, wrote, Die Hard Year One (unproduced script)]
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: Die Hard Year One (unproduced script)
Triple: [Ryan J. Condal, wrote, Die Hard Year One (unproduced script)]
Generated description
Die Hard Year One (unproduced script) is an unmade prequel screenplay in the Die Hard franchise that explores a younger John McClane in his early years as a New York City cop.

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_69f224da98f88190ab32f690cce5d303 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69c4e1a5081909b533baf10ce0142 completed May 3, 2026, 12:52 a.m.
NED1 Entity disambiguation (via context triple) batch_6a29592f0ffc81908f4513f97c21c52c completed June 10, 2026, 12:31 p.m.
NEDg Description generation batch_6a295d2ada288190aaf4ba844b770666 completed June 10, 2026, 12:48 p.m.
NED2 Entity disambiguation (via description) batch_6a295e13ebbc8190bba5d5efe052b6ea completed June 10, 2026, 12:52 p.m.
Created at: April 29, 2026, 9:10 p.m.