Triple

T27493473
Position Surface form Disambiguated ID Type / Status
Subject Theophilos E693955 entity
Predicate child P120 FINISHED
Object Anna (daughter of Theophilos)
Anna, daughter of the Byzantine emperor Theophilos, was a 9th-century Byzantine princess known primarily through her connection to the imperial Amorian dynasty.
E1777126 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: Anna (daughter of Theophilos) | Statement: [Theophilos, child, Anna (daughter of Theophilos)]
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: Anna (daughter of Theophilos)
Triple: [Theophilos, child, Anna (daughter of Theophilos)]
Generated description
Anna, daughter of the Byzantine emperor Theophilos, was a 9th-century Byzantine princess known primarily through her connection to the imperial Amorian dynasty.

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_69ef5382b9648190be0b1ef2ad5d043c completed April 27, 2026, 12:16 p.m.
NER Named-entity recognition batch_69f62e8bdb9c81909ec001884084e075 completed May 2, 2026, 5:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12c5a58e0c81909a1feefbe729d7b7 completed May 24, 2026, 9:32 a.m.
NEDg Description generation batch_6a12c6431ff8819092864b074cc494b8 completed May 24, 2026, 9:34 a.m.
NED2 Entity disambiguation (via description) batch_6a12c6c3a8fc819083942c89ff00352b completed May 24, 2026, 9:37 a.m.
Created at: April 27, 2026, 1:06 p.m.