Triple
T16313065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GLEN |
E396104
|
entity |
| Predicate | hasSettlementSystem |
P12493
|
FINISHED |
| Object | CREST |
E242865
|
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: CREST | Statement: [GLEN, hasSettlementSystem, CREST]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CREST Context triple: [GLEN, hasSettlementSystem, CREST]
-
A.
CREST
CREST is the UK’s central securities depository and electronic settlement system used to hold and settle trades in shares and other securities.
-
B.
Cres
Cres is a large Croatian island in the northern Adriatic Sea, known for its rugged coastline, pristine nature, and traditional coastal towns.
-
C.
CREI
CREI is a Barcelona-based research institute specializing in macroeconomics and international economics, closely linked to academic institutions such as Universitat Pompeu Fabra.
-
D.
Crest
chosen
Crest is a historic town in southeastern France’s Drôme department, best known for its medieval tower, one of the tallest castle keeps in Europe.
-
E.
Crest
Crest is a well-known oral care brand, particularly recognized for its toothpastes and whitening products.
- 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_69d87f23bb088190a16fbb91a1957ea5 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e288dc30f48190b508220429b66e92 |
completed | April 17, 2026, 7:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a001fa6ceb48190b937a15b94fd3cfa |
completed | May 10, 2026, 6:03 a.m. |
Created at: April 10, 2026, 5:06 a.m.