Triple

T32814939
Position Surface form Disambiguated ID Type / Status
Subject The Christmas Secret (2014) E839258 entity
Predicate hasMainCharacter P1183 FINISHED
Object Christine Eisley
Christine Eisley is the struggling single mother protagonist of the holiday drama film "The Christmas Secret," whose life is transformed by a series of unexpected events and acts of kindness.
E2034930 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: Christine Eisley | Statement: [The Christmas Secret (2014), hasMainCharacter, Christine Eisley]
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: Christine Eisley
Triple: [The Christmas Secret (2014), hasMainCharacter, Christine Eisley]
Generated description
Christine Eisley is the struggling single mother protagonist of the holiday drama film "The Christmas Secret," whose life is transformed by a series of unexpected events and acts of kindness.

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_69f3493df9008190a8f5d843dcd77704 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6cdce62d4819082dc7ea3214764e4 completed May 3, 2026, 4:23 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34e4f3a9388190b5f92e5145622005 completed June 19, 2026, 6:42 a.m.
NEDg Description generation batch_6a34e5e7ca0c8190b09741dfb9c7bdb0 completed June 19, 2026, 6:47 a.m.
NED2 Entity disambiguation (via description) batch_6a34e7032cac81909ef52e16456c9a15 completed June 19, 2026, 6:51 a.m.
Created at: May 1, 2026, 1:15 a.m.