Triple
T5599872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frederick of Naples |
E147089
|
entity |
| Predicate | title |
P38
|
FINISHED |
| Object |
Count of Copertino
Count of Copertino is a noble title historically associated with the Neapolitan royal lineage, notably held by Frederick of Naples.
|
E528913
|
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: Count of Copertino | Statement: [Frederick of Naples, title, Count of Copertino]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Count of Copertino Context triple: [Frederick of Naples, title, Count of Copertino]
-
A.
Cittareale
Cittareale is a small historic town in the Lazio region of central Italy, known for its mountainous setting in the Apennines and medieval origins.
-
B.
Melfi
Melfi is a historic town in southern Italy known as an important medieval center of Norman rule and site of several papal councils.
-
C.
Pisae
Pisae is the ancient Roman name for the city of Pisa in Tuscany, Italy, historically significant as a coastal settlement and later a prominent maritime republic.
-
D.
Cantù
Cantù is a town in the Lombardy region of northern Italy, known for its furniture-making tradition and location near Como.
-
E.
Grosseto
Grosseto is a Tuscan city near Italy’s western coast, known for its well-preserved medieval walls and role as the capital of the Maremma region.
- 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: Count of Copertino Triple: [Frederick of Naples, title, Count of Copertino]
Generated description
Count of Copertino is a noble title historically associated with the Neapolitan royal lineage, notably held by Frederick of Naples.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Count of Copertino Target entity description: Count of Copertino is a noble title historically associated with the Neapolitan royal lineage, notably held by Frederick of Naples.
-
A.
Cittareale
Cittareale is a small historic town in the Lazio region of central Italy, known for its mountainous setting in the Apennines and medieval origins.
-
B.
Melfi
Melfi is a historic town in southern Italy known as an important medieval center of Norman rule and site of several papal councils.
-
C.
Pisae
Pisae is the ancient Roman name for the city of Pisa in Tuscany, Italy, historically significant as a coastal settlement and later a prominent maritime republic.
-
D.
Cantù
Cantù is a town in the Lombardy region of northern Italy, known for its furniture-making tradition and location near Como.
-
E.
Grosseto
Grosseto is a Tuscan city near Italy’s western coast, known for its well-preserved medieval walls and role as the capital of the Maremma region.
- 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_69c009043d648190a7af89698ccf1e3e |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020d936dc8190a2e599f1df9fdd91 |
completed | March 22, 2026, 5:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0287139508190aa646918228cfdc0 |
completed | March 22, 2026, 5:35 p.m. |
| NEDg | Description generation | batch_69c0350eb53081909dc573fefa3e7f0a |
completed | March 22, 2026, 6:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c036ee4e1c8190b9e60655d72407ff |
completed | March 22, 2026, 6:37 p.m. |
Created at: March 22, 2026, 3:38 p.m.