Triple
T9703567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Somogy County |
E234838
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Marcali
Marcali is a small town in southwestern Hungary known for its agricultural surroundings and role as a local administrative and service center in Somogy County.
|
E817329
|
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: Marcali | Statement: [Somogy County, contains, Marcali]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marcali Context triple: [Somogy County, contains, Marcali]
-
A.
Makadara
Makadara is a residential and commercial neighborhood in Nairobi, Kenya, known for its dense population, vibrant local markets, and mix of low- to middle-income housing.
-
B.
Mella
Mella is a Spanish-language surname most notably associated with Cuban revolutionary leader Julio Antonio Mella.
-
C.
Barbalha
Barbalha is a municipality in northeastern Brazil known for its traditional cultural festivals and location in the state of Ceará.
-
D.
Lumarzo
Lumarzo is a small municipality in the Liguria region of northwestern Italy, located in the hilly inland area near Genoa.
-
E.
Sabanalarga
Sabanalarga is a rural municipality in eastern Colombia known for its cattle ranching and agricultural activities within the Llanos (plains) 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: Marcali Triple: [Somogy County, contains, Marcali]
Generated description
Marcali is a small town in southwestern Hungary known for its agricultural surroundings and role as a local administrative and service center in Somogy County.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marcali Target entity description: Marcali is a small town in southwestern Hungary known for its agricultural surroundings and role as a local administrative and service center in Somogy County.
-
A.
Makadara
Makadara is a residential and commercial neighborhood in Nairobi, Kenya, known for its dense population, vibrant local markets, and mix of low- to middle-income housing.
-
B.
Mella
Mella is a Spanish-language surname most notably associated with Cuban revolutionary leader Julio Antonio Mella.
-
C.
Barbalha
Barbalha is a municipality in northeastern Brazil known for its traditional cultural festivals and location in the state of Ceará.
-
D.
Lumarzo
Lumarzo is a small municipality in the Liguria region of northwestern Italy, located in the hilly inland area near Genoa.
-
E.
Sabanalarga
Sabanalarga is a rural municipality in eastern Colombia known for its cattle ranching and agricultural activities within the Llanos (plains) 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_69ca84cc78808190a56f3402b7c139a7 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9d73a0148190ad4178fd462cdd9c |
completed | April 1, 2026, 10:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d19f800ec48190bc3028ecb3baeb28 |
completed | April 4, 2026, 11:32 p.m. |
| NEDg | Description generation | batch_69d1a3cc5420819091ee338da5afe4b7 |
completed | April 4, 2026, 11:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1a5f265148190af432e3640221a33 |
completed | April 4, 2026, 11:59 p.m. |
Created at: March 30, 2026, 8:18 p.m.