Triple
T13553612
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Concesio |
E323711
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Teglie
Teglie is a locality or district that forms part of the municipality of Concesio in northern Italy.
|
E1047365
|
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: Teglie | Statement: [Concesio, hasSubdivision, Teglie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teglie Context triple: [Concesio, hasSubdivision, Teglie]
-
A.
Colander
Colander is a Python library used for data validation and deserialization, often employed in web applications to ensure structured and reliable input handling.
-
B.
Coltellini
Coltellini was an 18th-century Italian publisher and intellectual known for disseminating influential Enlightenment works, including Cesare Beccaria’s "Dei delitti e delle pene."
-
C.
Cijeruk
Cijeruk is a district in West Java, Indonesia, known for its hilly landscapes and proximity to the city of Bogor.
-
D.
Cugnoli
Cugnoli is a small Italian municipality in the Abruzzo region, known for its rural landscape and traditional local culture.
-
E.
Copper Bowl
The Copper Bowl was a college football postseason bowl game played annually in Arizona, later renamed the Insight Bowl and then the Cactus Bowl.
- 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: Teglie Triple: [Concesio, hasSubdivision, Teglie]
Generated description
Teglie is a locality or district that forms part of the municipality of Concesio in northern Italy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Teglie Target entity description: Teglie is a locality or district that forms part of the municipality of Concesio in northern Italy.
-
A.
Colander
Colander is a Python library used for data validation and deserialization, often employed in web applications to ensure structured and reliable input handling.
-
B.
Coltellini
Coltellini was an 18th-century Italian publisher and intellectual known for disseminating influential Enlightenment works, including Cesare Beccaria’s "Dei delitti e delle pene."
-
C.
Cijeruk
Cijeruk is a district in West Java, Indonesia, known for its hilly landscapes and proximity to the city of Bogor.
-
D.
Cugnoli
Cugnoli is a small Italian municipality in the Abruzzo region, known for its rural landscape and traditional local culture.
-
E.
Copper Bowl
The Copper Bowl was a college football postseason bowl game played annually in Arizona, later renamed the Insight Bowl and then the Cactus Bowl.
- 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_69d8076830b48190910a902bae5888e2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaff1c1f0819084352d9b2ee13d7a |
completed | April 12, 2026, 2:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75da721208190a3f5159125dbde9a |
completed | May 3, 2026, 2:37 p.m. |
| NEDg | Description generation | batch_69f75ec5101081909652b0c0998b36c8 |
completed | May 3, 2026, 2:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f75f4a3b0c81908c0ca0351771953b |
completed | May 3, 2026, 2:44 p.m. |
Created at: April 9, 2026, 9:46 p.m.