Triple
T502595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eastern France |
E10430
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Nancy
Nancy is a historic city in northeastern France renowned for its elegant 18th-century architecture and UNESCO-listed Place Stanislas.
|
E78951
|
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: Nancy | Statement: [Eastern France, containsCity, Nancy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nancy Context triple: [Eastern France, containsCity, Nancy]
-
A.
Nancy
Nancy is a feminine given name of Hebrew origin meaning "grace" that became especially popular in English-speaking countries in the 20th century.
-
B.
Nance
Nance is the middle name of John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
-
C.
Patricia
Patricia is a feminine given name of Latin origin, commonly used in English-speaking countries.
-
D.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
E.
Janet
Janet is a feminine given name commonly used in English-speaking countries, often associated with notable figures in entertainment and public life.
- 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: Nancy Triple: [Eastern France, containsCity, Nancy]
Generated description
Nancy is a historic city in northeastern France renowned for its elegant 18th-century architecture and UNESCO-listed Place Stanislas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nancy Target entity description: Nancy is a historic city in northeastern France renowned for its elegant 18th-century architecture and UNESCO-listed Place Stanislas.
-
A.
Nancy
Nancy is a feminine given name of Hebrew origin meaning "grace" that became especially popular in English-speaking countries in the 20th century.
-
B.
Nance
Nance is the middle name of John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
-
C.
Patricia
Patricia is a feminine given name of Latin origin, commonly used in English-speaking countries.
-
D.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
E.
Janet
Janet is a feminine given name commonly used in English-speaking countries, often associated with notable figures in entertainment and public life.
- 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_69a2e848adf881908e5e04f7af030093 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f1339748819089f89691a1698dd9 |
completed | Feb. 28, 2026, 1:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a5692e9f248190945be16aac260038 |
completed | March 2, 2026, 10:40 a.m. |
| NEDg | Description generation | batch_69a56ae958f481909b097848ef1d9b5d |
completed | March 2, 2026, 10:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a56b86d684819080c398847af0551b |
completed | March 2, 2026, 10:50 a.m. |
Created at: Feb. 28, 2026, 1:12 p.m.