Triple
T2198757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Let the Right One In |
E50438
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
EFTI
EFTI is a Swedish film production company known for its involvement in acclaimed Scandinavian cinema, including the horror drama "Let the Right One In."
|
E242702
|
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: EFTI | Statement: [Let the Right One In, productionCompany, EFTI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: EFTI Context triple: [Let the Right One In, productionCompany, EFTI]
-
A.
EIT
EIT is a European Union body that fosters innovation, entrepreneurship, and education by integrating business, research, and higher education institutions across Europe.
-
B.
TFI
TFI is the World Health Organization’s Tobacco Free Initiative, a program dedicated to reducing global tobacco use and its health impacts.
-
C.
TFI
TFI is the commonly used abbreviation for the Federal Supreme Court of Switzerland, the country’s highest judicial authority.
-
D.
EEF
EEF is the abbreviation for the Economist Educational Foundation, a charity that promotes economic and financial literacy through educational resources and programs.
-
E.
FTU
FTU is a leading Vietnamese university renowned for its programs in economics, business, and international trade.
- 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: EFTI Triple: [Let the Right One In, productionCompany, EFTI]
Generated description
EFTI is a Swedish film production company known for its involvement in acclaimed Scandinavian cinema, including the horror drama "Let the Right One In."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: EFTI Target entity description: EFTI is a Swedish film production company known for its involvement in acclaimed Scandinavian cinema, including the horror drama "Let the Right One In."
-
A.
EIT
EIT is a European Union body that fosters innovation, entrepreneurship, and education by integrating business, research, and higher education institutions across Europe.
-
B.
TFI
TFI is the World Health Organization’s Tobacco Free Initiative, a program dedicated to reducing global tobacco use and its health impacts.
-
C.
TFI
TFI is the commonly used abbreviation for the Federal Supreme Court of Switzerland, the country’s highest judicial authority.
-
D.
EEF
EEF is the abbreviation for the Economist Educational Foundation, a charity that promotes economic and financial literacy through educational resources and programs.
-
E.
FTU
FTU is a leading Vietnamese university renowned for its programs in economics, business, and international trade.
- 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_69a88b044ab48190add007487680f009 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbf7b65cc8190bcc5a5c52b90f33b |
completed | March 7, 2026, 6:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae5db9b0208190a63a75c86ea9dcff |
completed | March 9, 2026, 5:42 a.m. |
| NEDg | Description generation | batch_69ae5e866d108190b39853172d1ed1a6 |
completed | March 9, 2026, 5:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae5edfe80481908c3304c917c9065b |
completed | March 9, 2026, 5:47 a.m. |
Created at: March 4, 2026, 7:46 p.m.