Triple

T4654849
Position Surface form Disambiguated ID Type / Status
Subject TensorFlow Extended E102383 entity
Predicate alsoKnownAs P39 FINISHED
Object TFX
TFX is an end-to-end production machine learning platform built on TensorFlow that supports scalable data processing, model training, validation, and deployment.
 E457341 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: TFX | Statement: [TensorFlow Extended, alsoKnownAs, TFX]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TFX
Context triple: [TensorFlow Extended, alsoKnownAs, TFX]
  • A. TFX
    TFX is a French television channel owned and operated by the TF1 Group, offering a mix of entertainment, series, and reality programming.
  • B. TX-4
    TX-4 is the commonly used abbreviation for Texas's 4th congressional district in the United States House of Representatives.
  • C. FX2
    FX2 is a 1991 action-thriller film and sequel to the movie "F/X," starring Brian Dennehy and Bryan Brown as they again use movie special-effects skills to outwit criminals.
  • D. TRAX
    TRAX is the light rail system serving the Salt Lake City metropolitan area along Utah’s Wasatch Front.
  • E. F train
    The F train is a New York City Subway service that runs primarily along the IND Sixth Avenue Line in Manhattan, connecting neighborhoods in Queens, Manhattan, and Brooklyn.
  • 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: TFX
Triple: [TensorFlow Extended, alsoKnownAs, TFX]
Generated description
TFX is an end-to-end production machine learning platform built on TensorFlow that supports scalable data processing, model training, validation, and deployment.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TFX
Target entity description: TFX is an end-to-end production machine learning platform built on TensorFlow that supports scalable data processing, model training, validation, and deployment.
  • A. TFX
    TFX is a French television channel owned and operated by the TF1 Group, offering a mix of entertainment, series, and reality programming.
  • B. TX-4
    TX-4 is the commonly used abbreviation for Texas's 4th congressional district in the United States House of Representatives.
  • C. FX2
    FX2 is a 1991 action-thriller film and sequel to the movie "F/X," starring Brian Dennehy and Bryan Brown as they again use movie special-effects skills to outwit criminals.
  • D. TRAX
    TRAX is the light rail system serving the Salt Lake City metropolitan area along Utah’s Wasatch Front.
  • E. F train
    The F train is a New York City Subway service that runs primarily along the IND Sixth Avenue Line in Manhattan, connecting neighborhoods in Queens, Manhattan, and Brooklyn.
  • 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_69bd43d823288190952279faa0d1d066 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd6317ba70819089145766d3462e57 completed March 20, 2026, 3:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdfaef125c819097d79f25608302dc completed March 21, 2026, 1:57 a.m.
NEDg Description generation batch_69bdfc0964c881909e6b98a1c8ea747f completed March 21, 2026, 2:01 a.m.
NED2 Entity disambiguation (via description) batch_69bdfce1be788190ae3418df301e5136 completed March 21, 2026, 2:05 a.m.
Created at: March 20, 2026, 1:14 p.m.