Triple

T5102434
Position Surface form Disambiguated ID Type / Status
Subject Giga Shanghai E115010 entity
Predicate brand P1500 FINISHED
Object Tesla E3005 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: Tesla | Statement: [Giga Shanghai, brand, Tesla]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tesla
Context triple: [Giga Shanghai, brand, Tesla]
  • A. Tesla
    Tesla is a renowned Serbian-American inventor, electrical engineer, and futurist best known for his pioneering work on alternating current (AC) power systems and numerous innovations in electromagnetism.
  • B. Tesla, Inc. chosen
    Tesla, Inc. is an American electric vehicle and clean energy company known for pioneering mass-market electric cars, advanced battery technology, and autonomous driving innovations.
  • C. Tesla Semi
    The Tesla Semi is an all-electric Class 8 semi-truck designed to offer high efficiency, long range, and lower operating costs compared to traditional diesel trucks.
  • D. Tesla vehicles
    Tesla vehicles are a line of all-electric cars and SUVs produced by Tesla, Inc., known for their long range, high performance, advanced battery technology, and integrated autonomous driving features.
  • E. Tesla Vision
    Tesla Vision is Tesla’s camera-based driver-assistance and autonomous driving system that relies solely on vision processing instead of radar or lidar sensors.
  • 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_69bd4440b3348190be1251fd8b7951f1 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd7586a4a08190866aea6be625837c completed March 20, 2026, 4:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69bef7edd6548190acbdc74eb1d0028d completed March 21, 2026, 7:56 p.m.
Created at: March 20, 2026, 1:41 p.m.