Triple
T43837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | France |
E861
|
entity |
| Predicate | majorCity |
P316
|
FINISHED |
| Object |
Nice
Nice is a prominent Mediterranean coastal city on the French Riviera, known for its mild climate, beaches, and vibrant cultural life.
|
E2387
|
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: Nice | Statement: [France, majorCity, Nice]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nice Context triple: [France, majorCity, Nice]
-
A.
NE
NE is the common abbreviation for the New England Revolution, a professional Major League Soccer club based in the Greater Boston area.
-
B.
Grace
Grace is the central Christian concept of God’s unmerited favor and loving initiative toward humanity, enabling salvation and spiritual transformation.
-
C.
NAM
NAM is the commonly used acronym for the National Academy of Medicine, a leading U.S. nonprofit institution that provides expert advice on health, medicine, and biomedical science.
-
D.
Fair Deal
The Fair Deal was President Harry S. Truman’s ambitious post–World War II domestic reform program aimed at expanding social welfare, civil rights, and economic opportunity in the United States.
-
E.
Lee
Lee is a given name shared by numerous individuals across different cultures and professions.
- 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: Nice Triple: [France, majorCity, Nice]
Generated description
Nice is a prominent Mediterranean coastal city on the French Riviera, known for its mild climate, beaches, and vibrant cultural life.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nice Target entity description: Nice is a prominent Mediterranean coastal city on the French Riviera, known for its mild climate, beaches, and vibrant cultural life.
-
A.
NE
NE is the common abbreviation for the New England Revolution, a professional Major League Soccer club based in the Greater Boston area.
-
B.
Grace
Grace is the central Christian concept of God’s unmerited favor and loving initiative toward humanity, enabling salvation and spiritual transformation.
-
C.
NAM
NAM is the commonly used acronym for the National Academy of Medicine, a leading U.S. nonprofit institution that provides expert advice on health, medicine, and biomedical science.
-
D.
Fair Deal
The Fair Deal was President Harry S. Truman’s ambitious post–World War II domestic reform program aimed at expanding social welfare, civil rights, and economic opportunity in the United States.
-
E.
Lee
Lee is a given name shared by numerous individuals across different cultures and professions.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24ae36824819080e8336a3c9f9bf8 |
completed | Feb. 28, 2026, 1:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a24e63a9848190a0f38b23521804e6 |
completed | Feb. 28, 2026, 2:09 a.m. |
| NEDg | Description generation | batch_69a24efe21148190849accf51a2632f0 |
completed | Feb. 28, 2026, 2:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a24fcbeee4819087186dada7466dfe |
completed | Feb. 28, 2026, 2:15 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.