Triple
T539958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Strasbourg |
E12607
|
entity |
| Predicate | twinCity |
P1072
|
FINISHED |
| Object | Leicester |
E47527
|
NE FINISHED |
How this triple was built (2 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: Leicester | Statement: [Strasbourg, twinCity, Leicester]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leicester Context triple: [Strasbourg, twinCity, Leicester]
-
A.
Leicester
chosen
Leicester is a historic and culturally diverse city in the East Midlands of England, known for its Roman origins, vibrant multicultural community, and the rediscovery and reburial of King Richard III.
-
B.
Coventry
Coventry is a historic city in England, best known for its medieval cathedral destroyed in World War II and its symbolic postwar reconciliation efforts.
-
C.
Nottingham
Nottingham is a major city in the East Midlands of England, historically known for its lace-making and bicycle industries and famously associated with the legend of Robin Hood.
-
D.
Bournemouth
Bournemouth is a large coastal resort town on England’s south coast, known for its sandy beaches, tourism, and role as a regional commercial and transport hub.
-
E.
Luton
Luton is a large town in Bedfordshire, England, known for its international airport and diverse urban population.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a49334226c81908b0ea1689ef6aa3f |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4985e51908190a34aa82ea9dbee1e |
completed | March 1, 2026, 7:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a5dc8df9708190976967ad4e45a597 |
completed | March 2, 2026, 6:53 p.m. |
Created at: March 1, 2026, 7:32 p.m.