Triple
T13966889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arnside railway station |
E335947
|
entity |
| Predicate | stationCode |
P1289
|
FINISHED |
| Object |
ARN
ARN is the three-letter National Rail station code assigned to Arnside railway station in Cumbria, England.
|
E1072143
|
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: ARN | Statement: [Arnside railway station, stationCode, ARN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ARN Context triple: [Arnside railway station, stationCode, ARN]
-
A.
ARN
ARN is the three-letter IATA airport code for Stockholm Arlanda Airport, the main international gateway to Stockholm and one of Sweden’s busiest airports.
-
B.
ANK
ANK is a German vehicle registration code used for the district of Vorpommern-Greifswald in the state of Mecklenburg-Vorpommern.
-
C.
ARA
ARA is the station code for Ara Junction, a railway station in Bihar, India, on the Patna–Mughalsarai section of the Indian Railways network.
-
D.
ARA
ARA (arachidonic acid) is a long-chain omega-6 fatty acid important for infant brain and eye development and commonly added to baby formulas.
-
E.
ARA
ARA is the French administrative region of Auvergne-Rhône-Alpes, located in the southeast-central part of France and known for its diverse landscapes and major cities like Lyon and Grenoble.
- 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: ARN Triple: [Arnside railway station, stationCode, ARN]
Generated description
ARN is the three-letter National Rail station code assigned to Arnside railway station in Cumbria, England.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ARN Target entity description: ARN is the three-letter National Rail station code assigned to Arnside railway station in Cumbria, England.
-
A.
ARN
ARN is the three-letter IATA airport code for Stockholm Arlanda Airport, the main international gateway to Stockholm and one of Sweden’s busiest airports.
-
B.
ANK
ANK is a German vehicle registration code used for the district of Vorpommern-Greifswald in the state of Mecklenburg-Vorpommern.
-
C.
ARA
ARA is the station code for Ara Junction, a railway station in Bihar, India, on the Patna–Mughalsarai section of the Indian Railways network.
-
D.
ARA
ARA (arachidonic acid) is a long-chain omega-6 fatty acid important for infant brain and eye development and commonly added to baby formulas.
-
E.
ARA
ARA is the French administrative region of Auvergne-Rhône-Alpes, located in the southeast-central part of France and known for its diverse landscapes and major cities like Lyon and Grenoble.
- 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e8c9e988190a84c9ca8a78b515f |
completed | April 14, 2026, 12:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fba1da70588190a6c9a3895d92be5b |
completed | May 6, 2026, 8:17 p.m. |
| NEDg | Description generation | batch_69fba74a96b08190a08c51c231e49e5d |
completed | May 6, 2026, 8:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fba8585fb08190998d6a005dc0bd31 |
completed | May 6, 2026, 8:45 p.m. |
Created at: April 9, 2026, 10:18 p.m.