Triple
T9708322
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Centaurs |
E234955
|
entity |
| Predicate | notableMember |
P10
|
FINISHED |
| Object | Nessus |
E397005
|
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: Nessus | Statement: [Centaurs, notableMember, Nessus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nessus Context triple: [Centaurs, notableMember, Nessus]
-
A.
Nessus
chosen
Nessus is a centaur in Greek mythology best known for his role in the death of Heracles after deceitfully causing the poisoned garment incident.
-
B.
Nessus
Nessus is a widely used vulnerability scanning tool that identifies security weaknesses in networks, systems, and applications.
-
C.
OpenVAS
OpenVAS is an open-source vulnerability scanning and management framework used to assess and improve the security of computer networks and systems.
-
D.
Tenable
Tenable is a British daytime television quiz show, hosted by Warwick Davis, in which teams of contestants attempt to complete top-ten lists on a variety of topics.
-
E.
Greenbone Security Assistant
Greenbone Security Assistant is a web-based graphical user interface used to manage, configure, and review vulnerability scans within the OpenVAS security scanning framework.
- 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_69ca84cc78808190a56f3402b7c139a7 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9da5e09081909456909d768611e6 |
completed | April 1, 2026, 10:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d19f8476e08190865700679069dee6 |
completed | April 4, 2026, 11:32 p.m. |
Created at: March 30, 2026, 8:19 p.m.