Triple
T13943592
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reddish South railway station |
E335319
|
entity |
| Predicate | stationCode |
P1289
|
FINISHED |
| Object |
RDS
RDS is the National Rail station code for Reddish South railway station in Greater Manchester, England.
|
E1071776
|
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: RDS | Statement: [Reddish South railway station, stationCode, RDS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RDS Context triple: [Reddish South railway station, stationCode, RDS]
-
A.
RDS
RDS is a Canadian French-language sports television network that broadcasts a wide range of professional and amateur sporting events.
-
B.
RDS
RDS is a Microsoft Windows Server role that enables users to remotely access desktops and applications hosted on centralized servers.
-
C.
RDS2
RDS2 is a Swiss French-language television channel that serves as a secondary sports-focused outlet to the main Réseau des sports (RDS) network.
-
D.
Amazon RDS
Amazon RDS is a managed relational database service by Amazon Web Services that simplifies setup, operation, and scaling of databases in the cloud.
-
E.
ApsaraDB for RDS
ApsaraDB for RDS is Alibaba Cloud’s managed relational database service that provides scalable, high-availability SQL databases with automated management and security features.
- 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: RDS Triple: [Reddish South railway station, stationCode, RDS]
Generated description
RDS is the National Rail station code for Reddish South railway station in Greater Manchester, England.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: RDS Target entity description: RDS is the National Rail station code for Reddish South railway station in Greater Manchester, England.
-
A.
RDS
RDS is a Canadian French-language sports television network that broadcasts a wide range of professional and amateur sporting events.
-
B.
RDS
RDS is a Microsoft Windows Server role that enables users to remotely access desktops and applications hosted on centralized servers.
-
C.
RDS2
RDS2 is a Swiss French-language television channel that serves as a secondary sports-focused outlet to the main Réseau des sports (RDS) network.
-
D.
Amazon RDS
Amazon RDS is a managed relational database service by Amazon Web Services that simplifies setup, operation, and scaling of databases in the cloud.
-
E.
ApsaraDB for RDS
ApsaraDB for RDS is Alibaba Cloud’s managed relational database service that provides scalable, high-availability SQL databases with automated management and security features.
- 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_69d81c6081b88190b53e317c3370c8fe |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e0f6f3c8190a64058b732b0ac52 |
completed | April 14, 2026, 12:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fba1ca49a881909e77b5a2ae13265f |
completed | May 6, 2026, 8:17 p.m. |
| NEDg | Description generation | batch_69fba6e596a081909843ea5173e60af4 |
completed | May 6, 2026, 8:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fba74fe350819080eee658bca7eaf0 |
completed | May 6, 2026, 8:40 p.m. |
Created at: April 9, 2026, 10:17 p.m.