Triple
T14539238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mike Shapiro |
E341125
|
entity |
| Predicate | developed |
P73
|
FINISHED |
| Object | DTrace |
E68056
|
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: DTrace | Statement: [Mike Shapiro, developed, DTrace]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DTrace Context triple: [Mike Shapiro, developed, DTrace]
-
A.
DTrace
chosen
DTrace is a comprehensive dynamic tracing framework originally developed for Solaris that enables real-time, low-overhead observability and debugging of operating systems and applications.
-
B.
SystemTap
SystemTap is a Linux-based dynamic tracing and performance analysis tool that allows developers and administrators to monitor and diagnose kernel and user-space activity in real time.
-
C.
dotTrace
dotTrace is a performance and memory profiling tool for .NET applications created by JetBrains to help developers analyze and optimize their code.
-
D.
bpftrace
bpftrace is a high-level tracing language and tool for Linux that leverages eBPF to enable powerful, dynamic instrumentation and performance analysis of running systems.
-
E.
Dynatrace
Dynatrace is a software intelligence company that provides AI-powered application performance monitoring, observability, and infrastructure monitoring solutions for cloud environments.
- 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_69d822dac79c8190a84a073f3cbaced5 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb1bd0dd4819094c8b2f2aa6b1c5e |
completed | April 14, 2026, 9:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdf072a46481909287ef89f226270e |
completed | May 8, 2026, 2:17 p.m. |
Created at: April 10, 2026, 1:22 a.m.