Triple
T2671717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brussels Metro |
E55760
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Elisabeth
Elisabeth is a metro station on the Brussels Metro system in Brussels, Belgium.
|
E289847
|
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: Elisabeth | Statement: [Brussels Metro, hasStation, Elisabeth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elisabeth Context triple: [Brussels Metro, hasStation, Elisabeth]
-
A.
Elisabeth
Elisabeth is a feminine given name of Hebrew origin, commonly used in various European languages as a form of Elizabeth.
-
B.
Louise of Great Britain
Louise of Great Britain was a British princess who became Queen of Denmark and Norway through her marriage to King Frederick V.
-
C.
Mary Adelaide
Mary Adelaide was a British princess of the House of Hanover, mother of Queen Mary and grandmother of King George VI and great-grandmother of Queen Elizabeth II.
-
D.
Anne
Anne is the given name of Anne Morrow Lindbergh, the American author and aviator who was married to famed aviator Charles Lindbergh.
-
E.
Anne
Anne is a female given name of Hebrew origin, commonly used in many European languages and historically borne by numerous queens, saints, and notable women.
- 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: Elisabeth Triple: [Brussels Metro, hasStation, Elisabeth]
Generated description
Elisabeth is a metro station on the Brussels Metro system in Brussels, Belgium.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elisabeth Target entity description: Elisabeth is a metro station on the Brussels Metro system in Brussels, Belgium.
-
A.
Elisabeth
Elisabeth is a feminine given name of Hebrew origin, commonly used in various European languages as a form of Elizabeth.
-
B.
Louise of Great Britain
Louise of Great Britain was a British princess who became Queen of Denmark and Norway through her marriage to King Frederick V.
-
C.
Mary Adelaide
Mary Adelaide was a British princess of the House of Hanover, mother of Queen Mary and grandmother of King George VI and great-grandmother of Queen Elizabeth II.
-
D.
Anne
Anne is the given name of Anne Morrow Lindbergh, the American author and aviator who was married to famed aviator Charles Lindbergh.
-
E.
Anne
Anne is a female given name of Hebrew origin, commonly used in many European languages and historically borne by numerous queens, saints, and notable women.
- 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_69ab49e54de48190be708cd1cf8be073 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd98f98908190b5c6fb38d3d4367a |
completed | March 7, 2026, 7:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afaf54711881909936ecf850ff6e2e |
completed | March 10, 2026, 5:42 a.m. |
| NEDg | Description generation | batch_69afb000ed448190a3d6db802eb88958 |
completed | March 10, 2026, 5:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afb07e1d2c8190a8b8da3d3641b36c |
completed | March 10, 2026, 5:47 a.m. |
Created at: March 6, 2026, 9:54 p.m.