Triple

T30592144
Position Surface form Disambiguated ID Type / Status
Subject James Graham, 3rd Marquess of Montrose E778686 entity
Predicate spouse P13 FINISHED
Object Lady Christian Carnegie
Lady Christian Carnegie was a Scottish noblewoman of the 17th century who became Marchioness of Montrose through her marriage into the prominent Graham family.
E1921666 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: Lady Christian Carnegie | Statement: [James Graham, 3rd Marquess of Montrose, spouse, Lady Christian Carnegie]
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: Lady Christian Carnegie
Triple: [James Graham, 3rd Marquess of Montrose, spouse, Lady Christian Carnegie]
Generated description
Lady Christian Carnegie was a Scottish noblewoman of the 17th century who became Marchioness of Montrose through her marriage into the prominent Graham family.

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_69f224a1570c8190a85d3ac330479a79 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f6897bc52481908122b1af6cb45526 completed May 2, 2026, 11:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a28571289408190977e9214d19f8acf completed June 9, 2026, 6:10 p.m.
NEDg Description generation batch_6a2858aa2db881908e4481230846edf5 completed June 9, 2026, 6:17 p.m.
NED2 Entity disambiguation (via description) batch_6a285954e3208190a8bb4f6b023e11fd completed June 9, 2026, 6:20 p.m.
Created at: April 29, 2026, 8:24 p.m.