Triple
T4625318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stratford-upon-Avon railway station |
E101083
|
entity |
| Predicate | railCode |
P18202
|
FINISHED |
| Object |
SAV
SAV is the National Rail station code for Stratford-upon-Avon railway station in Warwickshire, England.
|
E456395
|
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: SAV | Statement: [Stratford-upon-Avon railway station, railCode, SAV]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SAV Context triple: [Stratford-upon-Avon railway station, railCode, SAV]
-
A.
SAU
SAU is an international university established by the South Asian Association for Regional Cooperation (SAARC) in New Delhi, India, focusing on postgraduate and doctoral education and research for students from South Asian countries.
-
B.
SAU
SAU is the three-letter ISO 3166-1 alpha-3 country code assigned to Saudi Arabia.
-
C.
SA3
SA3 is the 3GPP security working group responsible for specifying and evolving security architecture and mechanisms across mobile communication standards.
-
D.
sva
sva is the ISO 639-3 code for the Svan language, a Kartvelian language spoken by the Svan people in the Svaneti region of northwestern Georgia.
-
E.
SA2
SA2 is a 3GPP working group responsible for defining the overall system architecture and functional specifications of mobile communication networks.
- 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: SAV Triple: [Stratford-upon-Avon railway station, railCode, SAV]
Generated description
SAV is the National Rail station code for Stratford-upon-Avon railway station in Warwickshire, England.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SAV Target entity description: SAV is the National Rail station code for Stratford-upon-Avon railway station in Warwickshire, England.
-
A.
SAU
SAU is an international university established by the South Asian Association for Regional Cooperation (SAARC) in New Delhi, India, focusing on postgraduate and doctoral education and research for students from South Asian countries.
-
B.
SAU
SAU is the three-letter ISO 3166-1 alpha-3 country code assigned to Saudi Arabia.
-
C.
SA3
SA3 is the 3GPP security working group responsible for specifying and evolving security architecture and mechanisms across mobile communication standards.
-
D.
sva
sva is the ISO 639-3 code for the Svan language, a Kartvelian language spoken by the Svan people in the Svaneti region of northwestern Georgia.
-
E.
SA2
SA2 is a 3GPP working group responsible for defining the overall system architecture and functional specifications of mobile communication networks.
- 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_69bd43d0497c8190ac23c65c5804846a |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5a08ef488190af46418229309b0f |
completed | March 20, 2026, 2:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfaa564988190b565c26b9cd3d3be |
completed | March 21, 2026, 1:55 a.m. |
| NEDg | Description generation | batch_69bdfb6fa3fc8190b79b641025710eb1 |
completed | March 21, 2026, 1:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bdfbeddd7c8190955bd3363fec4ca1 |
completed | March 21, 2026, 2:01 a.m. |
Created at: March 20, 2026, 1:13 p.m.