Triple
T9289116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SEAQ |
E223470
|
entity |
| Predicate | relatedSystem |
P37
|
FINISHED |
| Object | SETS |
E42906
|
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: SETS | Statement: [SEAQ, relatedSystem, SETS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SETS Context triple: [SEAQ, relatedSystem, SETS]
-
A.
SETS
chosen
SETS is the London Stock Exchange’s central electronic order book system used for automated trading of the most liquid UK securities.
-
B.
Set
Set is an ancient Egyptian god associated primarily with chaos, storms, and disorder, often depicted as the adversary of his brother Osiris and the rival of Horus.
-
C.
NSSet
NSSet is an Objective-C collection class that represents an unordered, unique set of objects, commonly used in Cocoa and Cocoa Touch frameworks.
-
D.
SET
SET is an open-source penetration testing framework focused on social engineering attacks, commonly used by security professionals to simulate and assess human-targeted vulnerabilities.
-
E.
Powerset
Powerset was a natural-language search engine startup, later acquired by Microsoft, that focused on enabling more intuitive, semantic web search.
- 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_69ca8422ddf881908a3f8f876c9f53aa |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd0862b34c819097cb7c1777313925 |
completed | April 1, 2026, 11:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0c766fb408190a9f073f033652b6f |
completed | April 4, 2026, 8:10 a.m. |
Created at: March 30, 2026, 7:35 p.m.