Triple

T12160144
Position Surface form Disambiguated ID Type / Status
Subject Convair F-102 Delta Dagger E289681 entity
Predicate variant P4680 FINISHED
Object TF-102A
The TF-102A is a two-seat trainer version of the Convair F-102 Delta Dagger supersonic interceptor aircraft, used primarily for pilot instruction and transition training.
E967131 NE FINISHED

How this triple was built (4 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: TF-102A | Statement: [Convair F-102 Delta Dagger, variant, TF-102A]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: TF-102A
Context triple: [Convair F-102 Delta Dagger, variant, TF-102A]
  • A. Tianshou
    Tianshou is a reinforcement learning library for PyTorch that provides modular, efficient tools and algorithms for training and evaluating RL agents.
  • B. Tianshou
    Tianshou was the brief era name proclaimed by Empress Wu Zetian during her rule in the late 7th century Tang China.
  • C. Tian Wen
    Tian Wen is an ancient Chinese poetic work traditionally attributed to Qu Yuan, known for its rich mythological imagery and philosophical questioning.
  • D. Tian Rui
    Tian Rui is a chapter of the classical Daoist text Liezi, traditionally attributed to the philosopher Lie Yukou.
  • E. Tianyou
    Tianyou was the final era name of the Tang dynasty, marking its last years before the dynasty’s collapse in the early 10th century.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: TF-102A
Triple: [Convair F-102 Delta Dagger, variant, TF-102A]
Generated description
The TF-102A is a two-seat trainer version of the Convair F-102 Delta Dagger supersonic interceptor aircraft, used primarily for pilot instruction and transition training.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: TF-102A
Target entity description: The TF-102A is a two-seat trainer version of the Convair F-102 Delta Dagger supersonic interceptor aircraft, used primarily for pilot instruction and transition training.
  • A. Tianshou
    Tianshou is a reinforcement learning library for PyTorch that provides modular, efficient tools and algorithms for training and evaluating RL agents.
  • B. Tianshou
    Tianshou was the brief era name proclaimed by Empress Wu Zetian during her rule in the late 7th century Tang China.
  • C. Tian Wen
    Tian Wen is an ancient Chinese poetic work traditionally attributed to Qu Yuan, known for its rich mythological imagery and philosophical questioning.
  • D. Tian Rui
    Tian Rui is a chapter of the classical Daoist text Liezi, traditionally attributed to the philosopher Lie Yukou.
  • E. Tianyou
    Tianyou was the final era name of the Tang dynasty, marking its last years before the dynasty’s collapse in the early 10th century.
  • F. None of above. chosen

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_69d6ab4d6c00819095a9a7c35de83cfb completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d915c395e48190a16e97fd29787a51 completed April 10, 2026, 3:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f69e8498819080d571e6fb4edfde completed May 2, 2026, 1:05 p.m.
NEDg Description generation batch_69f601e0777081909e1212436680a10d completed May 2, 2026, 1:53 p.m.
NED2 Entity disambiguation (via description) batch_69f602a21f948190849839301f49d55a completed May 2, 2026, 1:56 p.m.
Created at: April 8, 2026, 9:50 p.m.