Triple
T3509071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | West Palm Beach |
E74150
|
entity |
| Predicate | hasSisterCity |
P919
|
FINISHED |
| Object |
Törökbálint
Törökbálint is a town in Pest County, Hungary, located just southwest of Budapest and known as a suburban residential area with growing commercial and industrial zones.
|
E365332
|
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: Törökbálint | Statement: [West Palm Beach, hasSisterCity, Törökbálint]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Törökbálint Context triple: [West Palm Beach, hasSisterCity, Törökbálint]
-
A.
Tiszaújváros
Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
-
B.
Komló
Komló is a town in southern Hungary known historically for its coal mining and hop-growing industries.
-
C.
Csákvár
Csákvár is a small town in central Hungary known for its rural character and location within the Transdanubian region.
-
D.
Tatabánya
Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
-
E.
Mátraháza
Mátraháza is a small mountain resort village in northern Hungary, known for its scenic location in the Mátra range and its hiking and wellness tourism.
- 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: Törökbálint Triple: [West Palm Beach, hasSisterCity, Törökbálint]
Generated description
Törökbálint is a town in Pest County, Hungary, located just southwest of Budapest and known as a suburban residential area with growing commercial and industrial zones.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Törökbálint Target entity description: Törökbálint is a town in Pest County, Hungary, located just southwest of Budapest and known as a suburban residential area with growing commercial and industrial zones.
-
A.
Tiszaújváros
Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
-
B.
Komló
Komló is a town in southern Hungary known historically for its coal mining and hop-growing industries.
-
C.
Csákvár
Csákvár is a small town in central Hungary known for its rural character and location within the Transdanubian region.
-
D.
Tatabánya
Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
-
E.
Mátraháza
Mátraháza is a small mountain resort village in northern Hungary, known for its scenic location in the Mátra range and its hiking and wellness tourism.
- 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc0cc394819087a9b598023f4f93 |
completed | March 8, 2026, 6:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b37e6da490819093a5f574ff0b6b00 |
completed | March 13, 2026, 3:03 a.m. |
| NEDg | Description generation | batch_69b37fb14a988190aa357622773e5817 |
completed | March 13, 2026, 3:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3805e16d08190883d286a6763c079 |
completed | March 13, 2026, 3:11 a.m. |
Created at: March 8, 2026, 3:18 p.m.