Triple

T31124228
Position Surface form Disambiguated ID Type / Status
Subject Callan Mulvey E793307 entity
Predicate portrayed P1668 FINISHED
Object Syllias in 300: Rise of an Empire
Syllias in 300: Rise of an Empire is a Spartan warrior and loyal ally who fights alongside the Greek forces against Xerxes’ invading Persian army.
E1947349 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: Syllias in 300: Rise of an Empire | Statement: [Callan Mulvey, portrayed, Syllias in 300: Rise of an Empire]
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: Syllias in 300: Rise of an Empire
Triple: [Callan Mulvey, portrayed, Syllias in 300: Rise of an Empire]
Generated description
Syllias in 300: Rise of an Empire is a Spartan warrior and loyal ally who fights alongside the Greek forces against Xerxes’ invading Persian army.

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_69f224d0a7688190af3fe3e6e26d01ed completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f6973a3e048190a5f2b112cfbb2d5f completed May 3, 2026, 12:30 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2938c32a6c8190b8a160152f04739c completed June 10, 2026, 10:13 a.m.
NEDg Description generation batch_6a293a06fef08190b9e8b9d10dba3cef completed June 10, 2026, 10:18 a.m.
NED2 Entity disambiguation (via description) batch_6a293a9c8cdc8190aceb7ddf2ae5e038 completed June 10, 2026, 10:21 a.m.
Created at: April 29, 2026, 9:05 p.m.