Triple

T6353057
Position Surface form Disambiguated ID Type / Status
Subject Lockheed F-104 Starfighter E142921 entity
Predicate variant P4680 FINISHED
Object TF-104G
The TF-104G is a two-seat trainer version of the Lockheed F-104 Starfighter, used primarily for advanced pilot instruction and conversion training.
 E586108 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-104G | Statement: [Lockheed F-104 Starfighter, variant, TF-104G]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TF-104G
Context triple: [Lockheed F-104 Starfighter, variant, TF-104G]
  • A. TX-10
    TX-10 is the commonly used abbreviation for Texas's 10th congressional district, a U.S. House of Representatives district covering parts of central Texas.
  • B. TX-4
    TX-4 is the commonly used abbreviation for Texas's 4th congressional district in the United States House of Representatives.
  • C. TFX
    TFX is an end-to-end production machine learning platform built on TensorFlow that supports scalable data processing, model training, validation, and deployment.
  • D. TFX
    TFX is a French television channel owned and operated by the TF1 Group, offering a mix of entertainment, series, and reality programming.
  • E. TF
    TF is the vehicle registration code used for motor vehicles registered in the Spanish province of Santa Cruz de Tenerife in the Canary Islands.
  • 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-104G
Triple: [Lockheed F-104 Starfighter, variant, TF-104G]
Generated description
The TF-104G is a two-seat trainer version of the Lockheed F-104 Starfighter, used primarily for advanced pilot instruction and conversion training.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TF-104G
Target entity description: The TF-104G is a two-seat trainer version of the Lockheed F-104 Starfighter, used primarily for advanced pilot instruction and conversion training.
  • A. TX-10
    TX-10 is the commonly used abbreviation for Texas's 10th congressional district, a U.S. House of Representatives district covering parts of central Texas.
  • B. TX-4
    TX-4 is the commonly used abbreviation for Texas's 4th congressional district in the United States House of Representatives.
  • C. TFX
    TFX is an end-to-end production machine learning platform built on TensorFlow that supports scalable data processing, model training, validation, and deployment.
  • D. TFX
    TFX is a French television channel owned and operated by the TF1 Group, offering a mix of entertainment, series, and reality programming.
  • E. TF
    TF is the vehicle registration code used for motor vehicles registered in the Spanish province of Santa Cruz de Tenerife in the Canary Islands.
  • 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_69c008d6dcbc8190aa1c2f1fd8916b42 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c067dec4a88190992d57a0cc7782ad completed March 22, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69c60459a7c081909b551dcf1735bf75 completed March 27, 2026, 4:15 a.m.
NEDg Description generation batch_69c605212688819081fc8f9c1500ff50 completed March 27, 2026, 4:18 a.m.
NED2 Entity disambiguation (via description) batch_69c605c0db308190960370a81ff8123b completed March 27, 2026, 4:21 a.m.
Created at: March 22, 2026, 4:31 p.m.