Triple
T3886201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Financial Secretary to the Treasury |
E92946
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
FST
FST is the commonly used abbreviation for the UK government ministerial post of Financial Secretary to the Treasury, a key role within HM Treasury responsible for economic and financial matters.
|
E395677
|
NE FINISHED |
How this triple was built (4 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: FST | Statement: [Financial Secretary to the Treasury, alsoKnownAs, FST]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FST Context triple: [Financial Secretary to the Treasury, alsoKnownAs, FST]
-
A.
FSL
FSL was the stock ticker symbol for Freescale Semiconductor, a former American manufacturer of embedded processors and semiconductor solutions.
-
B.
FAT
FAT is the three-letter IATA airport code for Fresno Yosemite International Airport in Fresno, California.
-
C.
FAT
FAT is the commonly used abbreviation for the FA Trophy, an English football knockout competition for non-league clubs.
-
D.
LFST
LFST is the ICAO airport code for Strasbourg Airport, an international airport serving the city of Strasbourg in northeastern France.
-
E.
FFS
FFS is the commonly used abbreviation for the Swiss Federal Railways, the national railway company of Switzerland.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: FST Triple: [Financial Secretary to the Treasury, alsoKnownAs, FST]
Generated description
FST is the commonly used abbreviation for the UK government ministerial post of Financial Secretary to the Treasury, a key role within HM Treasury responsible for economic and financial matters.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: FST Target entity description: FST is the commonly used abbreviation for the UK government ministerial post of Financial Secretary to the Treasury, a key role within HM Treasury responsible for economic and financial matters.
-
A.
FSL
FSL was the stock ticker symbol for Freescale Semiconductor, a former American manufacturer of embedded processors and semiconductor solutions.
-
B.
FAT
FAT is the three-letter IATA airport code for Fresno Yosemite International Airport in Fresno, California.
-
C.
FAT
FAT is the commonly used abbreviation for the FA Trophy, an English football knockout competition for non-league clubs.
-
D.
LFST
LFST is the ICAO airport code for Strasbourg Airport, an international airport serving the city of Strasbourg in northeastern France.
-
E.
FFS
FFS is the commonly used abbreviation for the Swiss Federal Railways, the national railway company of Switzerland.
- F. None of above. chosen
Provenance (5 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_69aed9697de0819087c2559295ff3d12 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeec942bfc8190a398fe370715a28b |
completed | March 9, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5125d0d208190a26ddd1bd4bca0d8 |
completed | March 14, 2026, 7:46 a.m. |
| NEDg | Description generation | batch_69b514fbe12881908359855dc67aa967 |
completed | March 14, 2026, 7:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b51551156c8190aebed2e51740f14d |
completed | March 14, 2026, 7:59 a.m. |
Created at: March 9, 2026, 3:20 p.m.