Triple
T8579071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hertford North railway station |
E203121
|
entity |
| Predicate | stationCode |
P1289
|
FINISHED |
| Object |
HFN
HFN is the three-letter National Rail station code assigned to Hertford North railway station in Hertfordshire, England.
|
E744227
|
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: HFN | Statement: [Hertford North railway station, stationCode, HFN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HFN Context triple: [Hertford North railway station, stationCode, HFN]
-
A.
FNM
FNM was the former stock ticker symbol for Fannie Mae, the U.S. government-sponsored enterprise that provides liquidity and stability to the mortgage market.
-
B.
HAF
HAF is the commonly used abbreviation for the Hellenic Air Force, the air warfare branch of Greece’s armed forces.
-
C.
FH
FH is the vehicle registration code used on license plates for the emirate of Fujairah in the United Arab Emirates.
-
D.
HVN
HVN is the ICAO airline designator used to identify Vietnam Airlines in international aviation operations.
-
E.
HVN
HVN is the IATA airport code for Tweed New Haven Airport, a regional airport serving New Haven, Connecticut.
- 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: HFN Triple: [Hertford North railway station, stationCode, HFN]
Generated description
HFN is the three-letter National Rail station code assigned to Hertford North railway station in Hertfordshire, England.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: HFN Target entity description: HFN is the three-letter National Rail station code assigned to Hertford North railway station in Hertfordshire, England.
-
A.
FNM
FNM was the former stock ticker symbol for Fannie Mae, the U.S. government-sponsored enterprise that provides liquidity and stability to the mortgage market.
-
B.
HAF
HAF is the commonly used abbreviation for the Hellenic Air Force, the air warfare branch of Greece’s armed forces.
-
C.
FH
FH is the vehicle registration code used on license plates for the emirate of Fujairah in the United Arab Emirates.
-
D.
HVN
HVN is the ICAO airline designator used to identify Vietnam Airlines in international aviation operations.
-
E.
HVN
HVN is the IATA airport code for Tweed New Haven Airport, a regional airport serving New Haven, Connecticut.
- 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_69ca8328ebe481909a8c038fa79959b4 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbea9a0708819084cb8b8d84017864 |
completed | March 31, 2026, 3:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce89a5d18c81908a21cf5e5944d6e1 |
completed | April 2, 2026, 3:22 p.m. |
| NEDg | Description generation | batch_69ce8ac1dba48190bbad47a762130aab |
completed | April 2, 2026, 3:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce8eae70008190b2c7bbe4ce8d4c0a |
completed | April 2, 2026, 3:43 p.m. |
Created at: March 30, 2026, 6:22 p.m.