Triple
T7992156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chelmsford railway station |
E186033
|
entity |
| Predicate | hasStationCode |
P1289
|
FINISHED |
| Object |
CHM
CHM is the National Rail station code for Chelmsford railway station in Essex, England.
|
E703703
|
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: CHM | Statement: [Chelmsford railway station, hasStationCode, CHM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CHM Context triple: [Chelmsford railway station, hasStationCode, CHM]
-
A.
Chimica
Chimica is a specialized scientific database focused on chemistry literature and research.
-
B.
Chemistry
Chemistry is the branch of science that studies the composition, structure, properties, and transformations of matter at the atomic and molecular levels.
-
C.
CHE
CHE is the three-letter ISO 3166-1 alpha-3 country code for Switzerland.
-
D.
CAS Common Chemistry
CAS Common Chemistry is a free online resource from the Chemical Abstracts Service that provides curated chemical information, including identifiers and basic data for commonly encountered substances.
-
E.
Elementa chemiae
Elementa chemiae is an influential early 18th-century chemistry textbook by Herman Boerhaave that systematically organized and advanced contemporary chemical knowledge.
- 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: CHM Triple: [Chelmsford railway station, hasStationCode, CHM]
Generated description
CHM is the National Rail station code for Chelmsford railway station in Essex, England.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: CHM Target entity description: CHM is the National Rail station code for Chelmsford railway station in Essex, England.
-
A.
Chimica
Chimica is a specialized scientific database focused on chemistry literature and research.
-
B.
Chemistry
Chemistry is the branch of science that studies the composition, structure, properties, and transformations of matter at the atomic and molecular levels.
-
C.
CHE
CHE is the three-letter ISO 3166-1 alpha-3 country code for Switzerland.
-
D.
CAS Common Chemistry
CAS Common Chemistry is a free online resource from the Chemical Abstracts Service that provides curated chemical information, including identifiers and basic data for commonly encountered substances.
-
E.
Elementa chemiae
Elementa chemiae is an influential early 18th-century chemistry textbook by Herman Boerhaave that systematically organized and advanced contemporary chemical knowledge.
- 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_69ca829c6c308190ab05b43d234c52b2 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3c712d0481908d163d2509d054fa |
completed | March 31, 2026, 3:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbe0fe312c81908c6874fa0aabe7d5 |
completed | March 31, 2026, 2:58 p.m. |
| NEDg | Description generation | batch_69cbe440a66c8190a5d5b417fb5082b7 |
completed | March 31, 2026, 3:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cc338a1c48819086ece073e04e8fa6 |
completed | March 31, 2026, 8:50 p.m. |
Created at: March 30, 2026, 5:16 p.m.