Triple
T20175835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiveton Bridge railway station |
E492092
|
entity |
| Predicate | stationCode |
P1289
|
FINISHED |
| Object |
KIV
KIV is the three-letter National Rail station code for Kiveton Bridge railway station in South Yorkshire, England.
|
E1416627
|
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: KIV | Statement: [Kiveton Bridge railway station, stationCode, KIV]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KIV Context triple: [Kiveton Bridge railway station, stationCode, KIV]
-
A.
KIJ
KIJ is the three-letter IATA airport code for Niigata Airport in Niigata, Japan.
-
B.
KI
KI is the regional vehicle registration code used on license plates to identify vehicles registered in Kyiv Oblast, Ukraine.
-
C.
KI
KI is the ISO 3166-1 alpha-2 country code for Kiribati, a Pacific island nation.
-
D.
KI
KI is the abbreviation for the Karolinska Institute, a renowned Swedish medical university known for its leading research and role in selecting Nobel laureates in Physiology or Medicine.
-
E.
KI
KI is the vehicle registration code used on license plates for the German city of Kiel.
- 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: KIV Triple: [Kiveton Bridge railway station, stationCode, KIV]
Generated description
KIV is the three-letter National Rail station code for Kiveton Bridge railway station in South Yorkshire, England.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: KIV Target entity description: KIV is the three-letter National Rail station code for Kiveton Bridge railway station in South Yorkshire, England.
-
A.
KIJ
KIJ is the three-letter IATA airport code for Niigata Airport in Niigata, Japan.
-
B.
KI
KI is the regional vehicle registration code used on license plates to identify vehicles registered in Kyiv Oblast, Ukraine.
-
C.
KI
KI is the ISO 3166-1 alpha-2 country code for Kiribati, a Pacific island nation.
-
D.
KI
KI is the vehicle registration code used on license plates for the German city of Kiel.
-
E.
KI
KI is the abbreviation for the Karolinska Institute, a renowned Swedish medical university known for its leading research and role in selecting Nobel laureates in Physiology or Medicine.
- 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_69da6266c6888190bc1a3ecf24814d34 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e668eb50a48190af7de53680ca2f5d |
completed | April 20, 2026, 5:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a083c77d38c8190929be133825ed8e7 |
completed | May 16, 2026, 9:44 a.m. |
| NEDg | Description generation | batch_6a083d86085c8190a71d0dce65c659a4 |
completed | May 16, 2026, 9:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a083eb380f48190bda8549d122f830b |
completed | May 16, 2026, 9:53 a.m. |
Created at: April 11, 2026, 11:36 p.m.