Triple
T5182771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bolton railway station |
E116959
|
entity |
| Predicate | stationCode |
P1289
|
FINISHED |
| Object |
BON
BON is the National Rail station code for Bolton railway station in Greater Manchester, England.
|
E501454
|
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: BON | Statement: [Bolton railway station, stationCode, BON]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BON Context triple: [Bolton railway station, stationCode, BON]
-
A.
Bon
Bon is an ancient Tibetan spiritual tradition and religion, distinct from but historically intertwined with Buddhism, that encompasses rituals, cosmology, and practices rooted in the pre-Buddhist culture of Tibet.
-
B.
BOL
BOL is the three-letter ISO 3166-1 alpha-3 country code assigned to Bolivia.
-
C.
Bun
Bun is a modern, high-performance JavaScript runtime and toolkit designed as an alternative to Node.js and Deno, featuring a built-in bundler, test runner, and package manager.
-
D.
Bou
Bou is a surname of Spanish origin borne by Argentine professional footballer Gustavo Bou, among others.
-
E.
Bun B
Bun B is an American rapper best known as one half of the influential Southern hip hop duo UGK and for his extensive solo work and guest appearances.
- 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: BON Triple: [Bolton railway station, stationCode, BON]
Generated description
BON is the National Rail station code for Bolton railway station in Greater Manchester, England.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: BON Target entity description: BON is the National Rail station code for Bolton railway station in Greater Manchester, England.
-
A.
Bon
Bon is an ancient Tibetan spiritual tradition and religion, distinct from but historically intertwined with Buddhism, that encompasses rituals, cosmology, and practices rooted in the pre-Buddhist culture of Tibet.
-
B.
BOL
BOL is the three-letter ISO 3166-1 alpha-3 country code assigned to Bolivia.
-
C.
Bun
Bun is a modern, high-performance JavaScript runtime and toolkit designed as an alternative to Node.js and Deno, featuring a built-in bundler, test runner, and package manager.
-
D.
Bou
Bou is a surname of Spanish origin borne by Argentine professional footballer Gustavo Bou, among others.
-
E.
Bun B
Bun B is an American rapper best known as one half of the influential Southern hip hop duo UGK and for his extensive solo work and guest appearances.
- 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_69bd446140f08190becb93c61158f27f |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd799d50388190bf2b7dfdd90949e9 |
completed | March 20, 2026, 4:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bee07d20208190a423a9a395ac9d32 |
completed | March 21, 2026, 6:16 p.m. |
| NEDg | Description generation | batch_69bee695358c8190801c63cff67efd1b |
completed | March 21, 2026, 6:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bee6f91df48190adfcf47a63f4c8ef |
completed | March 21, 2026, 6:44 p.m. |
Created at: March 20, 2026, 1:46 p.m.