Triple
T18704793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TFX |
E457341
|
entity |
| Predicate | includesComponent |
P1393
|
FINISHED |
| Object | BulkInferrer |
—
|
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: BulkInferrer | Statement: [TFX, includesComponent, BulkInferrer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BulkInferrer Context triple: [TFX, includesComponent, BulkInferrer]
-
A.
BulkInferrer
chosen
BulkInferrer is a TensorFlow Extended (TFX) component used to run large-scale batch inference with trained machine learning models on sizable datasets.
-
B.
Dataphernes
Dataphernes was a Persian noble and military officer who played a role in the capture of the usurper Bessus during Alexander the Great’s campaign.
-
C.
Data2Vec
Data2Vec is a self-supervised learning framework developed by Meta AI that learns unified contextual representations across modalities such as speech, vision, and text.
-
D.
Mendata
Mendata is a small rural municipality located in the Busturialdea comarca of the Basque Country in northern Spain.
-
E.
Datu
Datu is a traditional title for a chieftain or local ruler in pre-colonial Philippine societies.
- 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.