Triple
T20458744
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ADF Data Controls |
E501864
|
entity |
| Predicate | usedWith |
P4791
|
FINISHED |
| Object | ADF Faces |
—
|
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: ADF Faces | Statement: [ADF Data Controls, usedWith, ADF Faces]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ADF Faces Context triple: [ADF Data Controls, usedWith, ADF Faces]
-
A.
ADF Faces
chosen
ADF Faces is a JavaServer Faces (JSF)-based UI component framework from Oracle used to build rich, enterprise web applications, often as part of the Oracle ADF technology stack.
-
B.
Facelets
Facelets is a powerful view declaration language for building component-based user interfaces in Java web applications, particularly with Jakarta Server Faces.
-
C.
MyFaces
MyFaces is an open-source implementation of the Jakarta Server Faces (JSF) framework that provides components and tools for building Java-based web user interfaces.
-
D.
Jakarta Server Faces
Jakarta Server Faces is a component-based web framework for building server-side user interfaces in Java enterprise applications.
-
E.
Faces
Faces is a critically acclaimed 2014 mixtape by American rapper Mac Miller, known for its introspective lyrics, experimental production, and exploration of themes like addiction and mental health.
- 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_69e0b4ad4940819098cf2ff6413574e5 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e696a4652c8190acf79fa2e285e436 |
completed | April 20, 2026, 9:12 p.m. |
Created at: April 16, 2026, 11:33 a.m.