Triple
T8048486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Persian Gulf Pro League |
E187613
|
entity |
| Predicate | notableClub |
P8182
|
FINISHED |
| Object |
Tractor SC
Tractor SC is a prominent Iranian football club based in Tabriz, widely supported and known for competing at the top level of Iranian football.
|
E706485
|
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: Tractor SC | Statement: [Persian Gulf Pro League, notableClub, Tractor SC]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tractor SC Context triple: [Persian Gulf Pro League, notableClub, Tractor SC]
-
A.
Lokomotiv
Lokomotiv is a Russian professional football club based in Moscow that competes in the Russian Premier League.
-
B.
Astra Ploiești
Astra Ploiești is a Romanian professional football club historically associated with the city of Ploiești and known for competing in the country’s top leagues.
-
C.
Neoplan
Neoplan is a German bus and coach manufacturer renowned for its innovative, high-end touring and city buses.
-
D.
Traktor Chelyabinsk
Traktor Chelyabinsk is a professional ice hockey club from Chelyabinsk, Russia, historically recognized as one of the prominent teams in Soviet and Russian hockey.
-
E.
Verdy Kawasaki
Verdy Kawasaki was the former name of Tokyo Verdy, one of Japan’s most historic professional football clubs.
- 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: Tractor SC Triple: [Persian Gulf Pro League, notableClub, Tractor SC]
Generated description
Tractor SC is a prominent Iranian football club based in Tabriz, widely supported and known for competing at the top level of Iranian football.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tractor SC Target entity description: Tractor SC is a prominent Iranian football club based in Tabriz, widely supported and known for competing at the top level of Iranian football.
-
A.
Lokomotiv
Lokomotiv is a Russian professional football club based in Moscow that competes in the Russian Premier League.
-
B.
Astra Ploiești
Astra Ploiești is a Romanian professional football club historically associated with the city of Ploiești and known for competing in the country’s top leagues.
-
C.
Neoplan
Neoplan is a German bus and coach manufacturer renowned for its innovative, high-end touring and city buses.
-
D.
Traktor Chelyabinsk
Traktor Chelyabinsk is a professional ice hockey club from Chelyabinsk, Russia, historically recognized as one of the prominent teams in Soviet and Russian hockey.
-
E.
Verdy Kawasaki
Verdy Kawasaki was the former name of Tokyo Verdy, one of Japan’s most historic professional football clubs.
- 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_69ca82b15e948190a62fd7af5218426a |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3f7711f48190af2002533c2e426a |
completed | March 31, 2026, 3:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc5716934481908ec60cc9fd825ad7 |
completed | March 31, 2026, 11:21 p.m. |
| NEDg | Description generation | batch_69cc58acba3c8190b7d09aa23b5f10f8 |
completed | March 31, 2026, 11:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cc5ccee5648190a8ebdf8029eded98 |
completed | March 31, 2026, 11:46 p.m. |
Created at: March 30, 2026, 5:24 p.m.