Triple
T17606979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jennifer Blanc |
E428857
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Everly
Everly is a 2014 action-thriller film starring Salma Hayek as a woman fighting off waves of assassins in her apartment after turning on her mobster ex.
|
E1277848
|
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: Everly | Statement: [Jennifer Blanc, notableWork, Everly]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Everly Context triple: [Jennifer Blanc, notableWork, Everly]
-
A.
Ladywood
Ladywood is an inner-city district of Birmingham, England, known for its dense residential areas, regeneration projects, and proximity to the city centre.
-
B.
Earline
Earline is a character in Ishmael Reed's satirical novel "Mumbo Jumbo," which explores themes of African American culture, history, and resistance.
-
C.
Ellies
The Ellies are annual awards recognizing excellence in magazine journalism and publishing, presented by the American Society of Magazine Editors.
-
D.
Tilly
Tilly is one of the short stories included in James Joyce’s collection *Pomes Penyeach*.
-
E.
Tilly
Tilly is the commonly used name for Johann Tserclaes, Count of Tilly, a prominent general of the Catholic League during the early stages of the Thirty Years' War.
- 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: Everly Triple: [Jennifer Blanc, notableWork, Everly]
Generated description
Everly is a 2014 action-thriller film starring Salma Hayek as a woman fighting off waves of assassins in her apartment after turning on her mobster ex.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Everly Target entity description: Everly is a 2014 action-thriller film starring Salma Hayek as a woman fighting off waves of assassins in her apartment after turning on her mobster ex.
-
A.
Ladywood
Ladywood is an inner-city district of Birmingham, England, known for its dense residential areas, regeneration projects, and proximity to the city centre.
-
B.
Earline
Earline is a character in Ishmael Reed's satirical novel "Mumbo Jumbo," which explores themes of African American culture, history, and resistance.
-
C.
Ellies
The Ellies are annual awards recognizing excellence in magazine journalism and publishing, presented by the American Society of Magazine Editors.
-
D.
Tilly
Tilly is one of the short stories included in James Joyce’s collection *Pomes Penyeach*.
-
E.
Tilly
Tilly is the commonly used name for Johann Tserclaes, Count of Tilly, a prominent general of the Catholic League during the early stages of the Thirty Years' War.
- 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46c4ccef08190aeaa88670364bd74 |
completed | April 19, 2026, 5:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01e8201678819094fc43c1fcde7203 |
completed | May 11, 2026, 2:30 p.m. |
| NEDg | Description generation | batch_6a01ecfb2ff4819082f67ab1f2ce8885 |
completed | May 11, 2026, 2:51 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01ee0c03e881908aaaa3bd0f596387 |
completed | May 11, 2026, 2:56 p.m. |
Created at: April 10, 2026, 5:51 a.m.