Triple
T19352305
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Npcap |
E484052
|
entity |
| Predicate | replaces |
P101
|
FINISHED |
| Object | WinPcap |
—
|
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: WinPcap | Statement: [Npcap, replaces, WinPcap]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WinPcap Context triple: [Npcap, replaces, WinPcap]
-
A.
WinPcap
chosen
WinPcap is a Windows packet capture and network monitoring library that provides low-level network access for tools like Wireshark.
-
B.
Npcap
Npcap is a high-performance packet capture and network monitoring library for Windows, commonly used by tools like Wireshark for low-level network traffic analysis.
-
C.
libpcap
libpcap is a widely used packet capture library and file format for recording and analyzing network traffic across various tools and platforms.
-
D.
Wireshark
Wireshark is a widely used open-source network protocol analyzer that captures and interactively inspects traffic on computer networks for troubleshooting, analysis, and security auditing.
-
E.
Berkeley Packet Filter
Berkeley Packet Filter is a low-level, in-kernel virtual machine and instruction set originally designed for efficient packet capture and filtering in Unix-like operating systems.
- 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_69d8e8d244f8819080eb1f3491300db2 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e61905b16881909ab0e932bb9a0cda |
completed | April 20, 2026, 12:16 p.m. |
Created at: April 10, 2026, 1:34 p.m.