Triple
T2833811
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olympique Lyonnais Féminin |
E62301
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
OL
OL is the commonly used abbreviation for Olympique Lyonnais, a major French football club best known internationally for its highly successful women's team.
|
E304419
|
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: OL | Statement: [Olympique Lyonnais Féminin, nickname, OL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: OL Context triple: [Olympique Lyonnais Féminin, nickname, OL]
-
A.
OL
OL is a UK postcode area covering Oldham and surrounding parts of Greater Manchester and nearby regions in North West England.
-
B.
OLE
OLE (Object Linking and Embedding) is a Microsoft technology that enables embedding and linking to documents and other objects within different applications, forming a foundation for later component technologies like ActiveX.
-
C.
OLA
OLA is the commonly used acronym for the United Nations Office of Legal Affairs, which provides legal advice and support to UN organs and specialized agencies.
-
D.
OH
OH is the official United States Postal Service abbreviation for the state of Ohio.
-
E.
LOS
LOS is the IATA airport code for Murtala Muhammed International Airport, the main international gateway serving Lagos, Nigeria.
- 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: OL Triple: [Olympique Lyonnais Féminin, nickname, OL]
Generated description
OL is the commonly used abbreviation for Olympique Lyonnais, a major French football club best known internationally for its highly successful women's team.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: OL Target entity description: OL is the commonly used abbreviation for Olympique Lyonnais, a major French football club best known internationally for its highly successful women's team.
-
A.
OL
OL is a UK postcode area covering Oldham and surrounding parts of Greater Manchester and nearby regions in North West England.
-
B.
OLE
OLE (Object Linking and Embedding) is a Microsoft technology that enables embedding and linking to documents and other objects within different applications, forming a foundation for later component technologies like ActiveX.
-
C.
OLA
OLA is the commonly used acronym for the United Nations Office of Legal Affairs, which provides legal advice and support to UN organs and specialized agencies.
-
D.
OH
OH is the official United States Postal Service abbreviation for the state of Ohio.
-
E.
LOS
LOS is the IATA airport code for Murtala Muhammed International Airport, the main international gateway serving Lagos, Nigeria.
- 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_69ab4c3c39188190955b9c49d98463d8 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdec18b808190aedae2ed11d53b15 |
completed | March 7, 2026, 8:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afe8bf82808190a556e22d518f46f7 |
completed | March 10, 2026, 9:47 a.m. |
| NEDg | Description generation | batch_69afea1732b481909a8df01d80ca1bd4 |
completed | March 10, 2026, 9:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b00eff94b481909a4cc08c8494870c |
completed | March 10, 2026, 12:30 p.m. |
Created at: March 6, 2026, 10:01 p.m.