Triple
T5972619
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Federation of European Securities Exchanges |
E132909
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | FESE |
E559743
|
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: FESE | Statement: [Federation of European Securities Exchanges, shortName, FESE]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FESE Context triple: [Federation of European Securities Exchanges, shortName, FESE]
-
A.
FESE
chosen
FESE is the main industry association representing European securities exchanges and other market operators at the European and global level.
-
B.
FAES
FAES is the acronym for the Armed Forces of El Salvador, the country's unified military institution responsible for national defense and security.
-
C.
FSE
FSE is the Faculty of Science and Engineering at the University of Groningen, encompassing a broad range of natural sciences, engineering, and technology disciplines.
-
D.
FSE
FSE is a premier international research conference on software engineering organized under ACM SIGSOFT.
-
E.
FSE
FSE (Fast Software Encryption) is a leading international research conference focused on the design and analysis of symmetric-key cryptographic primitives and algorithms.
- 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_69c0086deab081908550159ca23eec9b |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04a00c3588190b335d7d3341b6d68 |
completed | March 22, 2026, 7:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c108422220819092ac63e5ebb264b4 |
completed | March 23, 2026, 9:30 a.m. |
Created at: March 22, 2026, 4:03 p.m.