Triple

T28563732
Position Surface form Disambiguated ID Type / Status
Subject Zannanza E722616 entity
Predicate event P1664 FINISHED
Object Zannanza affair
The Zannanza affair was a Late Bronze Age diplomatic crisis in which the Hittite prince Zannanza was murdered en route to marry an Egyptian queen, straining relations between the Hittite and Egyptian empires.
E1841417 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: Zannanza affair | Statement: [Zannanza, event, Zannanza affair]
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: Zannanza affair
Triple: [Zannanza, event, Zannanza affair]
Generated description
The Zannanza affair was a Late Bronze Age diplomatic crisis in which the Hittite prince Zannanza was murdered en route to marry an Egyptian queen, straining relations between the Hittite and Egyptian empires.

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_69f01a5f69d08190ad5c0d2167078dec completed April 28, 2026, 2:24 a.m.
NER Named-entity recognition batch_69f6508cd720819085534e8463334767 completed May 2, 2026, 7:29 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25058c48c88190b79fa0a056615cc9 completed June 7, 2026, 5:45 a.m.
NEDg Description generation batch_6a2509ba31c8819082af4190ac01be51 completed June 7, 2026, 6:03 a.m.
NED2 Entity disambiguation (via description) batch_6a250e35cb2c81909d7632be22680434 completed June 7, 2026, 6:22 a.m.
Created at: April 28, 2026, 4:06 a.m.