Triple
T7982500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Macron Stadium |
E185604
|
entity |
| Predicate | sponsor |
P67
|
FINISHED |
| Object | Macron |
E188813
|
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: Macron | Statement: [Macron Stadium, sponsor, Macron]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Macron Context triple: [Macron Stadium, sponsor, Macron]
-
A.
Macron
chosen
Macron is an Italian sportswear company known for designing and manufacturing athletic apparel and equipment for professional and amateur teams worldwide.
-
B.
Emmanuel Macron
Emmanuel Macron is a French politician and former investment banker who has served as President of France since 2017, known for his centrist, pro-European Union stance.
-
C.
Jean-Michel Macron
Jean-Michel Macron is a French neurologist and the father of Emmanuel Macron, the President of France.
-
D.
Édouard Philippe
Édouard Philippe is a French politician who served as Prime Minister of France from 2017 to 2020 under President Emmanuel Macron.
-
E.
François Hollande
François Hollande is a French Socialist politician who served as President of France from 2012 to 2017.
- 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_69ca829a2cfc819083d591d58ec04075 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3c2a1aa881909c3cea280dff38f5 |
completed | March 31, 2026, 3:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc567cfe548190bbd163a32c340bcc |
completed | March 31, 2026, 11:19 p.m. |
Created at: March 30, 2026, 5:15 p.m.