Triple

T30480262
Position Surface form Disambiguated ID Type / Status
Subject Patricia Riggen E775562 entity
Predicate notableWork P4 FINISHED
Object Under the Same Moon
Under the Same Moon is a 2007 drama film that follows a young Mexican boy’s perilous journey across the U.S.–Mexico border to reunite with his undocumented mother working in Los Angeles.
 E1917960 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: Under the Same Moon | Statement: [Patricia Riggen, notableWork, Under the Same Moon]
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: Under the Same Moon
Triple: [Patricia Riggen, notableWork, Under the Same Moon]
Generated description
Under the Same Moon is a 2007 drama film that follows a young Mexican boy’s perilous journey across the U.S.–Mexico border to reunite with his undocumented mother working in Los Angeles.

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_69f22497341481909c21ba329fadaa6b completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f687415610819081818d08f7c79a81 completed May 2, 2026, 11:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27ac24977081908ef68cf6c9550937 completed June 9, 2026, 6:01 a.m.
NEDg Description generation batch_6a27b03526708190a7c646581213f15d completed June 9, 2026, 6:18 a.m.
NED2 Entity disambiguation (via description) batch_6a27b0d083a88190839b0c015391e69e completed June 9, 2026, 6:21 a.m.
Created at: April 29, 2026, 8:12 p.m.