Triple
T15592069
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stadio Artemio Franchi |
E374767
|
entity |
| Predicate | hasNickname |
P39
|
FINISHED |
| Object |
Franchi
Franchi is the commonly used nickname for Stadio Artemio Franchi, the main football stadium in Florence, Italy.
|
E1165375
|
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: Franchi | Statement: [Stadio Artemio Franchi, hasNickname, Franchi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Franchi Context triple: [Stadio Artemio Franchi, hasNickname, Franchi]
-
A.
Franchi
Franchi is an Italian firearms manufacturer best known for its shotguns, operating as a subsidiary of the Beretta Holding group.
-
B.
Beretta
Beretta is a historic Italian firearms manufacturer renowned worldwide for its pistols, shotguns, and military small arms.
-
C.
Benelli Armi
Benelli Armi is an Italian firearms manufacturer best known for its high-quality semi-automatic shotguns used in hunting, sport shooting, and law enforcement worldwide.
-
D.
Oerlikon
Oerlikon is a district in the north of Zurich, Switzerland, known as a major residential, commercial, and transportation hub of the city.
-
E.
Diemaco
Diemaco is a Canadian firearms manufacturer best known for producing and developing variants of the AR-15/M16 family of rifles for military and law enforcement use.
- 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: Franchi Triple: [Stadio Artemio Franchi, hasNickname, Franchi]
Generated description
Franchi is the commonly used nickname for Stadio Artemio Franchi, the main football stadium in Florence, Italy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Franchi Target entity description: Franchi is the commonly used nickname for Stadio Artemio Franchi, the main football stadium in Florence, Italy.
-
A.
Franchi
Franchi is an Italian firearms manufacturer best known for its shotguns, operating as a subsidiary of the Beretta Holding group.
-
B.
Beretta
Beretta is a historic Italian firearms manufacturer renowned worldwide for its pistols, shotguns, and military small arms.
-
C.
Benelli Armi
Benelli Armi is an Italian firearms manufacturer best known for its high-quality semi-automatic shotguns used in hunting, sport shooting, and law enforcement worldwide.
-
D.
Oerlikon
Oerlikon is a district in the north of Zurich, Switzerland, known as a major residential, commercial, and transportation hub of the city.
-
E.
Diemaco
Diemaco is a Canadian firearms manufacturer best known for producing and developing variants of the AR-15/M16 family of rifles for military and law enforcement use.
- 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_69d85cce25008190b13b52745fbd719b |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e5e43d48190a8fd367f13f1c7e1 |
completed | April 16, 2026, 2:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff4c55fa248190b114a5b63560f87b |
completed | May 9, 2026, 3:01 p.m. |
| NEDg | Description generation | batch_69ff50020d748190be36f3c08df43e40 |
completed | May 9, 2026, 3:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff50a349688190ab7a18fa4460d86e |
completed | May 9, 2026, 3:20 p.m. |
Created at: April 10, 2026, 4:12 a.m.