Triple
T8005425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stade de Reims |
E186352
|
entity |
| Predicate | rival |
P437
|
FINISHED |
| Object | AS Nancy Lorraine |
E78951
|
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: AS Nancy Lorraine | Statement: [Stade de Reims, rival, AS Nancy Lorraine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AS Nancy Lorraine Context triple: [Stade de Reims, rival, AS Nancy Lorraine]
-
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.
Nancy
chosen
Nancy is a historic city in northeastern France renowned for its elegant 18th-century architecture and UNESCO-listed Place Stanislas.
-
C.
Nanci
Nanci is a feminine given name most notably associated with the late American folk and country singer-songwriter Nanci Griffith.
-
D.
Nancy Grey
Nancy Grey is a fictional character from the film "Red Dog," contributing to the story’s emotional depth and relationships surrounding the legendary kelpie.
-
E.
Nancy Malone
Nancy Malone was an American television director, producer, and former child actress known for her pioneering work behind the camera in a male-dominated industry.
- 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_69ca82aaaf24819084b94d18f699ba53 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3cf72fc08190aa78b97c1ab92f90 |
completed | March 31, 2026, 3:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbe127afe0819092d5ad0c430fadc4 |
completed | March 31, 2026, 2:58 p.m. |
Created at: March 30, 2026, 5:18 p.m.