Triple
T22563558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Georgi Benkovski Stadium |
E557880
|
entity |
| Predicate | hasTenant |
P3277
|
FINISHED |
| Object |
Hebar Pazardzhik
Hebar Pazardzhik is a Bulgarian professional football club based in the city of Pazardzhik.
|
E328786
|
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: Hebar Pazardzhik | Statement: [Georgi Benkovski Stadium, hasTenant, Hebar Pazardzhik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hebar Pazardzhik Context triple: [Georgi Benkovski Stadium, hasTenant, Hebar Pazardzhik]
-
A.
Pazardzhik
Pazardzhik is a city in southern Bulgaria known as a regional economic and cultural center in the Upper Thracian Plain.
-
B.
Botevgrad
Botevgrad is a town in western Bulgaria named in honor of the national revolutionary and poet Hristo Botev.
-
C.
Velbazhd
Velbazhd is the historical name of the Bulgarian town of Kyustendil, known as the site of a major medieval battle between Serbian and Bulgarian forces.
-
D.
Blagoevgrad
Blagoevgrad is a city in southwestern Bulgaria known as a regional cultural and educational center, home to several universities and a vibrant student population.
-
E.
Sapareva Banya
Sapareva Banya is a Bulgarian spa town renowned for its hot mineral springs and the hottest geyser in continental Europe.
- 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: Hebar Pazardzhik Triple: [Georgi Benkovski Stadium, hasTenant, Hebar Pazardzhik]
Generated description
Hebar Pazardzhik is a Bulgarian professional football club based in the city of Pazardzhik.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hebar Pazardzhik Target entity description: Hebar Pazardzhik is a Bulgarian professional football club based in the city of Pazardzhik.
-
A.
Pazardzhik
chosen
Pazardzhik is a city in southern Bulgaria known as a regional economic and cultural center in the Upper Thracian Plain.
-
B.
Botevgrad
Botevgrad is a town in western Bulgaria named in honor of the national revolutionary and poet Hristo Botev.
-
C.
Velbazhd
Velbazhd is the historical name of the Bulgarian town of Kyustendil, known as the site of a major medieval battle between Serbian and Bulgarian forces.
-
D.
Blagoevgrad
Blagoevgrad is a city in southwestern Bulgaria known as a regional cultural and educational center, home to several universities and a vibrant student population.
-
E.
Sapareva Banya
Sapareva Banya is a Bulgarian spa town renowned for its hot mineral springs and the hottest geyser in continental Europe.
- F. None of above.
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_69e11e5ae4ac8190b1f503457603d969 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15fa7a828819096804ac928e2aaf9 |
completed | April 29, 2026, 1:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0bae72fec08190a224971ca3330681 |
completed | May 19, 2026, 12:27 a.m. |
| NEDg | Description generation | batch_6a0bafa985508190b3206760d58c5018 |
completed | May 19, 2026, 12:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0bb052e8e881909cc9495be15cfb9f |
completed | May 19, 2026, 12:35 a.m. |
Created at: April 16, 2026, 8:52 p.m.