Triple
T540391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lazio |
E12614
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Frosinone
Frosinone is a city in central Italy that serves as the capital of the province of the same name within the Lazio region.
|
E101395
|
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: Frosinone | Statement: [Lazio, containsCity, Frosinone]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Frosinone Context triple: [Lazio, containsCity, Frosinone]
-
A.
Lazio
Lazio is a central Italian region best known for encompassing the nation’s capital, Rome, and its rich historical and cultural heritage.
-
B.
Pescara
Pescara is a coastal city in the Abruzzo region of central Italy, known for its Adriatic beaches, modern urban layout, and role as a commercial and tourist hub.
-
C.
Umbria
Umbria is a central Italian region known for its historic hill towns, medieval architecture, and rich cultural heritage.
-
D.
Rimini
Rimini is a historic Italian coastal city on the Adriatic Sea, renowned for its beaches, Roman and Renaissance landmarks, and vibrant tourism industry.
-
E.
Molise
Molise is a small, predominantly rural region in southern Italy known for its mountainous landscapes, traditional agriculture, and relatively low population density.
- 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: Frosinone Triple: [Lazio, containsCity, Frosinone]
Generated description
Frosinone is a city in central Italy that serves as the capital of the province of the same name within the Lazio region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Frosinone Target entity description: Frosinone is a city in central Italy that serves as the capital of the province of the same name within the Lazio region.
-
A.
Lazio
Lazio is a central Italian region best known for encompassing the nation’s capital, Rome, and its rich historical and cultural heritage.
-
B.
Pescara
Pescara is a coastal city in the Abruzzo region of central Italy, known for its Adriatic beaches, modern urban layout, and role as a commercial and tourist hub.
-
C.
Umbria
Umbria is a central Italian region known for its historic hill towns, medieval architecture, and rich cultural heritage.
-
D.
Rimini
Rimini is a historic Italian coastal city on the Adriatic Sea, renowned for its beaches, Roman and Renaissance landmarks, and vibrant tourism industry.
-
E.
Molise
Molise is a small, predominantly rural region in southern Italy known for its mountainous landscapes, traditional agriculture, and relatively low population density.
- 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_69a49334226c81908b0ea1689ef6aa3f |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4985feee481908184a39210feab95 |
completed | March 1, 2026, 7:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7a3a260888190b89e90c1da061733 |
completed | March 4, 2026, 3:14 a.m. |
| NEDg | Description generation | batch_69a7a5d4125481908fa8cbfefe39f7cd |
completed | March 4, 2026, 3:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a7a6584acc81908a12da6f0c2faec5 |
completed | March 4, 2026, 3:26 a.m. |
Created at: March 1, 2026, 7:32 p.m.