Triple
T14329984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Open Your Eyes |
E355319
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Sogetel
Sogetel is a film and television production company known for producing European, particularly French-language, cinematic works.
|
E1093834
|
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: Sogetel | Statement: [Open Your Eyes, productionCompany, Sogetel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sogetel Context triple: [Open Your Eyes, productionCompany, Sogetel]
-
A.
GTE Mobilnet
GTE Mobilnet was a major U.S. cellular telephone service provider that operated mobile networks before eventually becoming part of Verizon Wireless.
-
B.
Telkom
Telkom is a major South African telecommunications company that provides fixed-line, mobile, and data services across the country.
-
C.
Telico
Telico is a small unincorporated community located in Ellis County, Texas.
-
D.
Globe Telecom
Globe Telecom is a major Philippine telecommunications company that provides mobile, internet, and other digital communication services nationwide.
-
E.
Tigo
Tigo is a multinational telecommunications company that provides mobile, internet, and digital services across several countries in Latin America and Africa.
- 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: Sogetel Triple: [Open Your Eyes, productionCompany, Sogetel]
Generated description
Sogetel is a film and television production company known for producing European, particularly French-language, cinematic works.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sogetel Target entity description: Sogetel is a film and television production company known for producing European, particularly French-language, cinematic works.
-
A.
GTE Mobilnet
GTE Mobilnet was a major U.S. cellular telephone service provider that operated mobile networks before eventually becoming part of Verizon Wireless.
-
B.
Telkom
Telkom is a major South African telecommunications company that provides fixed-line, mobile, and data services across the country.
-
C.
Telico
Telico is a small unincorporated community located in Ellis County, Texas.
-
D.
Globe Telecom
Globe Telecom is a major Philippine telecommunications company that provides mobile, internet, and other digital communication services nationwide.
-
E.
Tigo
Tigo is a multinational telecommunications company that provides mobile, internet, and digital services across several countries in Latin America and Africa.
- 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_69d8278fa2108190bc0d0e7939c1eb03 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8c1def0081908f03cda8e84d20c0 |
completed | April 14, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd46943dac819092f5935d9d312949 |
completed | May 8, 2026, 2:12 a.m. |
| NEDg | Description generation | batch_69fd4811e2808190b559d8348079ae8f |
completed | May 8, 2026, 2:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd48d827488190b4a494d4da64ba51 |
completed | May 8, 2026, 2:22 a.m. |
Created at: April 10, 2026, 1:13 a.m.