Triple
T19248596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hallonbergen metro station |
E481326
|
entity |
| Predicate | hasStationCode |
P1289
|
FINISHED |
| Object |
HBG
HBG is the station code for Hallonbergen metro station on the Stockholm Metro system in Sweden.
|
E1366459
|
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: HBG | Statement: [Hallonbergen metro station, hasStationCode, HBG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HBG Context triple: [Hallonbergen metro station, hasStationCode, HBG]
-
A.
HBG
HBG was a major Dutch construction and civil engineering company known for large-scale infrastructure and building projects in the Netherlands and abroad.
-
B.
HGB
HGB is the abbreviation for the German Commercial Code, the central body of law governing commercial transactions and business entities in Germany.
-
C.
BHB
BHB is the stock exchange of the Kingdom of Bahrain, providing a regulated marketplace for trading securities and other financial instruments.
-
D.
HGF
HGF is the abbreviation for the Helmholtz Association, Germany’s largest scientific research organization spanning multiple disciplines and large-scale facilities.
-
E.
HGF
HGF is the National Rail station code for Hag Fold railway station in Greater Manchester, England.
- 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: HBG Triple: [Hallonbergen metro station, hasStationCode, HBG]
Generated description
HBG is the station code for Hallonbergen metro station on the Stockholm Metro system in Sweden.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: HBG Target entity description: HBG is the station code for Hallonbergen metro station on the Stockholm Metro system in Sweden.
-
A.
HBG
HBG was a major Dutch construction and civil engineering company known for large-scale infrastructure and building projects in the Netherlands and abroad.
-
B.
HGB
HGB is the abbreviation for the German Commercial Code, the central body of law governing commercial transactions and business entities in Germany.
-
C.
BHB
BHB is the stock exchange of the Kingdom of Bahrain, providing a regulated marketplace for trading securities and other financial instruments.
-
D.
HGF
HGF is the abbreviation for the Helmholtz Association, Germany’s largest scientific research organization spanning multiple disciplines and large-scale facilities.
-
E.
HGF
HGF is the National Rail station code for Hag Fold railway station in Greater Manchester, England.
- 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_69d8e8cd9d1081908a181d02b88b59b8 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fb2e7cf881908dafd45d7a305c52 |
completed | April 20, 2026, 10:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0705e2d3948190b1ff731f648f68c7 |
completed | May 15, 2026, 11:39 a.m. |
| NEDg | Description generation | batch_6a0706bd92b48190848fcf5be679d207 |
completed | May 15, 2026, 11:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a070763ca5c81908afa73b30645223a |
completed | May 15, 2026, 11:45 a.m. |
Created at: April 10, 2026, 1:27 p.m.