Triple

T26061494
Position Surface form Disambiguated ID Type / Status
Subject Kingdom of Ma'in E657273 entity
Predicate neighbour P17964 FINISHED
Object Kingdom of Qataban
The Kingdom of Qataban was an ancient South Arabian kingdom centered in present-day Yemen, known for its role in the incense trade and its distinctive Sabaic culture and inscriptions.
E1739969 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: Kingdom of Qataban | Statement: [Kingdom of Ma'in, neighbour, Kingdom of Qataban]
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: Kingdom of Qataban
Triple: [Kingdom of Ma'in, neighbour, Kingdom of Qataban]
Generated description
The Kingdom of Qataban was an ancient South Arabian kingdom centered in present-day Yemen, known for its role in the incense trade and its distinctive Sabaic culture and inscriptions.

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_69ee5bbd788481909e22bd7153d0c037 completed April 26, 2026, 6:38 p.m.
NER Named-entity recognition batch_69f60692a4f081909cd9d0ca75d590a0 completed May 2, 2026, 2:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1209178bbc81909def797421c1752e completed May 23, 2026, 8:07 p.m.
NEDg Description generation batch_6a120a2acf54819094d2f16637877bb7 completed May 23, 2026, 8:12 p.m.
NED2 Entity disambiguation (via description) batch_6a120b0abaf8819087003d4c7978853f completed May 23, 2026, 8:16 p.m.
Created at: April 26, 2026, 7:18 p.m.