Triple
T2254509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Limoges |
E49689
|
entity |
| Predicate | hasSportsTeam |
P330
|
FINISHED |
| Object | Limoges CSP |
E49689
|
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: Limoges CSP | Statement: [Limoges, hasSportsTeam, Limoges CSP]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Limoges CSP Context triple: [Limoges, hasSportsTeam, Limoges CSP]
-
A.
Limoges
chosen
Limoges is a historic city in central France renowned for its fine porcelain production and medieval architecture.
-
B.
Sèvres
Sèvres is a commune in the southwestern suburbs of Paris, France, historically notable as the site where the post–World War I Treaty of Sèvres was concluded.
-
C.
Montesson
Montesson is a suburban commune in the Yvelines department of north-central France, located to the northwest of Paris along the Seine River.
-
D.
Aubusson
Aubusson is a town in central France renowned for its centuries-old tradition of tapestry and carpet weaving.
-
E.
Lusser
Lusser is a German surname most notably associated with engineer Robert Lusser, known for his contributions to aeronautics and reliability engineering.
- 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_69a88aaa9250819095e127d0d77e8a32 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc121af78819085b2e601d2f9bcdf |
completed | March 7, 2026, 6:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6b1fc8808190aebc534ea5adb534 |
completed | March 9, 2026, 6:39 a.m. |
Created at: March 4, 2026, 7:47 p.m.