Triple
T780381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frankfurt am Main |
E16481
|
entity |
| Predicate | sisterCity |
P1072
|
FINISHED |
| Object | Birmingham |
E13669
|
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: Birmingham | Statement: [Frankfurt am Main, sisterCity, Birmingham]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Birmingham Context triple: [Frankfurt am Main, sisterCity, Birmingham]
-
A.
Birmingham
chosen
Birmingham is a major industrial city in England’s West Midlands, historically significant for its manufacturing heritage and heavy bombing during the Second World War.
-
B.
Birmingham
Birmingham is a major industrial and cultural city in the southern United States, known historically for its steel production and pivotal role in the Civil Rights Movement.
-
C.
Manchester
Manchester is a major city in northwest England known for its industrial heritage, vibrant cultural scene, and influential contributions to music, sport, and science.
-
D.
Manchester
Manchester is the most populous city in the U.S. state of New Hampshire and a major economic and cultural center for the region.
-
E.
Wolverhampton
Wolverhampton is a large industrial city in England’s West Midlands, known historically for its role in the coal, steel, and manufacturing industries.
- 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_69a4936ad1fc81908f190208059ccf78 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a750c6708190a3f2c3abf16b4ea4 |
completed | March 1, 2026, 8:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac118d9b408190b1dd9540386406f2 |
completed | March 7, 2026, 11:52 a.m. |
Created at: March 1, 2026, 7:37 p.m.