Triple
T18704792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TFX |
E457341
|
entity |
| Predicate | includesComponent |
P1393
|
FINISHED |
| Object | Pusher |
—
|
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: Pusher | Statement: [TFX, includesComponent, Pusher]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pusher Context triple: [TFX, includesComponent, Pusher]
-
A.
Pusher
chosen
Pusher is a TensorFlow Extended (TFX) component responsible for validating and deploying trained machine learning models to serving infrastructure.
-
B.
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.
-
C.
Pusher-v2
Pusher-v2 is a MuJoCo-based reinforcement learning benchmark environment where an agent controls a robotic arm to push an object to a target location.
-
D.
Pusher III
Pusher III is a 2005 Danish crime film directed by Nicolas Winding Refn, serving as the final installment in his gritty Copenhagen-set Pusher trilogy.
-
E.
Pusher II
Pusher II is a Danish crime drama film that follows a small-time criminal’s desperate struggle to escape his violent past and dysfunctional family in Copenhagen’s underworld.
- 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_69d8d392aad081909fe31aa03e6e97d1 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5671665bc8190b9b4a4ce4ec5b2eb |
completed | April 19, 2026, 11:36 p.m. |
Created at: April 10, 2026, 11:49 a.m.