Triple
T9899352
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Azure IoT Hub |
E182245
|
entity |
| Predicate | hasSDK |
P82523
|
FINISHED |
| Object | Java |
E13745
|
NE FINISHED |
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: Java | Statement: [Azure IoT Hub, hasSDK, Java]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Java Context triple: [Azure IoT Hub, hasSDK, Java]
-
A.
Java
Java is a large, densely populated island in Indonesia that has long served as the country’s political and economic center.
-
B.
Java
chosen
Java is a widely used, object-oriented programming language known for its platform independence and extensive use in enterprise, web, and mobile application development.
-
C.
Java (Dzau)
Java (Dzau) is a town in South Ossetia that serves as an important regional center and transport hub in the mountainous area north of Tskhinvali.
-
D.
Javo
Javo is a character from the game Monkey Grip, likely depicted as a distinctive figure within its cast.
-
E.
Javakade
Javakade is a waterfront street on Amsterdam’s Java Island known for its modern residential architecture and harborside views.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca82876f8081909cf75df0f99bb13f |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cdb4adc03481909e0f657db01e5bab |
completed | April 2, 2026, 12:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1eb1b9534819093c5150f1ed8f685 |
completed | April 5, 2026, 4:54 a.m. |
Created at: March 30, 2026, 8:40 p.m.