Triple
T21615930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiszombor |
E533435
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Mako
Makó is a town in southeastern Hungary renowned for its onion cultivation and thermal baths.
|
E1492922
|
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: Mako | Statement: [Kiszombor, locatedNear, Mako]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mako Context triple: [Kiszombor, locatedNear, Mako]
-
A.
Mako
Mako was a Japanese-American actor and voice actor known for his distinctive voice and roles in films like "Conan the Barbarian" and as the voice of Iroh in "Avatar: The Last Airbender."
-
B.
Mako
Mako is a Japanese imperial family member best known as Princess Mako of Akishino, the former princess who left royal status upon her marriage to a commoner.
-
C.
Mako
Mako is a high-speed steel roller coaster at SeaWorld Orlando themed around the ocean’s fastest shark.
-
D.
Mako
Mako is the nickname of Benjamin Mako Hill, a prominent free software activist, scholar, and developer involved with projects like Debian and Wikimedia.
-
E.
Mako
Mako is a central firebending protagonist in *The Legend of Korra*, known for his serious demeanor, leadership in Team Avatar, and complex romantic relationships.
- 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: Mako Triple: [Kiszombor, locatedNear, Mako]
Generated description
Makó is a town in southeastern Hungary renowned for its onion cultivation and thermal baths.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mako Target entity description: Makó is a town in southeastern Hungary renowned for its onion cultivation and thermal baths.
-
A.
Mako
Mako is a beverage brand commonly associated with bottled drinks.
-
B.
Mako
Mako is a high-speed steel roller coaster at SeaWorld Orlando themed around the ocean’s fastest shark.
-
C.
Mako
Mako is a Japanese imperial family member best known as Princess Mako of Akishino, the former princess who left royal status upon her marriage to a commoner.
-
D.
Mako
Mako is a central firebending protagonist in *The Legend of Korra*, known for his serious demeanor, leadership in Team Avatar, and complex romantic relationships.
-
E.
Mako
Mako is the nickname of Benjamin Mako Hill, a prominent free software activist, scholar, and developer involved with projects like Debian and Wikimedia.
- 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_69e0c46411108190bba0d4176dffc9f3 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef3baab9e88190bc02f27133ef32d6 |
completed | April 27, 2026, 10:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09fd419ad48190a1a748a377f1a719 |
completed | May 17, 2026, 5:39 p.m. |
| NEDg | Description generation | batch_6a09ff300f808190aa3c84c067b290bc |
completed | May 17, 2026, 5:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09ffc0c3f08190a2d386515223ec73 |
completed | May 17, 2026, 5:49 p.m. |
Created at: April 16, 2026, 6:33 p.m.