Triple

T5281058
Position Surface form Disambiguated ID Type / Status
Subject Muna al-Hussein E119497 entity
Predicate familyName P18 FINISHED
Object Gardiner E158953 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: Gardiner | Statement: [Muna al-Hussein, familyName, Gardiner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gardiner
Context triple: [Muna al-Hussein, familyName, Gardiner]
  • A. Gardiner chosen
    Gardiner is an English surname historically associated with Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
  • B. Orono
    Orono is a suburban city in Minnesota known for its affluent residential communities and scenic location along the north shore of Lake Minnetonka.
  • C. Orono
    Orono is a small rural village in Ontario, Canada, known for its historic downtown, agricultural surroundings, and community events.
  • D. Gardiner, Maine
    Gardiner, Maine is a small historic city in central Maine located along the Kennebec River, known for its preserved downtown and 19th-century architecture.
  • E. Wadsworth
    Wadsworth is a small unincorporated community in Nevada known for its location along the Truckee River and its historical ties to the transcontinental railroad.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69bd446d05a8819092ad333a3f9c8d5c completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd84c5212481909cb3b5f43c0eedc0 completed March 20, 2026, 5:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf06dd56a08190a7cfef614e5990f4 completed March 21, 2026, 9 p.m.
Created at: March 20, 2026, 1:52 p.m.