Triple

T17561552
Position Surface form Disambiguated ID Type / Status
Subject Dataflow worker E427704 entity
Predicate readsFrom P43634 FINISHED
Object Pub/Sub NE NERFINISHED

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: Pub/Sub | Statement: [Dataflow worker, readsFrom, Pub/Sub]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pub/Sub
Context triple: [Dataflow worker, readsFrom, Pub/Sub]
  • A. Google Cloud Pub/Sub chosen
    Google Cloud Pub/Sub is a fully managed real-time messaging service that enables asynchronous, scalable communication between independent applications and services.
  • B. Pub/Sub Lite
    Pub/Sub Lite is a lower-cost, high-throughput messaging service on Google Cloud designed for streaming data workloads that can tolerate more operational management compared to standard Pub/Sub.
  • C. Publish–Subscribe pattern
    The Publish–Subscribe pattern is a messaging design pattern in which senders (publishers) broadcast messages without knowledge of specific receivers, and subscribers receive only the messages they have expressed interest in.
  • D. Pusher
    Pusher is a 1996 Danish crime thriller film directed by Nicolas Winding Refn that launched Mads Mikkelsen’s film career and became a cult classic.
  • E. Pusher
    Pusher is a TensorFlow Extended (TFX) component responsible for validating and deploying trained machine learning models to serving infrastructure.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d889e0385081908a04b66f4dd4bd0d completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e456274c888190ac80402e391674dd completed April 19, 2026, 4:12 a.m.
Created at: April 10, 2026, 5:50 a.m.