Triple
T13054708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hannah John-Kamen |
E327539
|
entity |
| Predicate | characterPortrayed |
P1507
|
FINISHED |
| Object |
Osha
Osha is a character portrayed by actress Hannah John-Kamen, known from the science fiction series "Killjoys."
|
E1018916
|
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: Osha | Statement: [Hannah John-Kamen, characterPortrayed, Osha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Osha Context triple: [Hannah John-Kamen, characterPortrayed, Osha]
-
A.
Otse
Otse is a village located in Botswana’s South-East District, known for its traditional culture and proximity to the capital, Gaborone.
-
B.
Orsa
Orsa is a small locality and municipality in central Sweden known for its forests, lakes, and traditional Dalarna culture.
-
C.
Osanna
Osanna is a choral movement within J.S. Bach’s Mass in B minor, known for its exuberant double-chorus writing and festive character.
-
D.
O'Steen
O'Steen is a surname most notably associated with American film editor Sam O'Steen, known for his work on several acclaimed Hollywood films.
-
E.
Oga
Oga is a coastal city in northern Japan known for the Oga Peninsula and its traditional Namahage folklore.
- 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: Osha Triple: [Hannah John-Kamen, characterPortrayed, Osha]
Generated description
Osha is a character portrayed by actress Hannah John-Kamen, known from the science fiction series "Killjoys."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Osha Target entity description: Osha is a character portrayed by actress Hannah John-Kamen, known from the science fiction series "Killjoys."
-
A.
Otse
Otse is a village located in Botswana’s South-East District, known for its traditional culture and proximity to the capital, Gaborone.
-
B.
Orsa
Orsa is a small locality and municipality in central Sweden known for its forests, lakes, and traditional Dalarna culture.
-
C.
Osanna
Osanna is a choral movement within J.S. Bach’s Mass in B minor, known for its exuberant double-chorus writing and festive character.
-
D.
O'Steen
O'Steen is a surname most notably associated with American film editor Sam O'Steen, known for his work on several acclaimed Hollywood films.
-
E.
Oga
Oga is a coastal city in northern Japan known for the Oga Peninsula and its traditional Namahage folklore.
- 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_69d8076e64308190904fb5c93517c901 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d980bb52d88190b5be12000e27a2c9 |
completed | April 10, 2026, 10:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbdead348190aa7aaa29c371d72a |
completed | May 3, 2026, 4:15 a.m. |
| NEDg | Description generation | batch_69f6d039254881909927b58225f194de |
completed | May 3, 2026, 4:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6d0d218d4819080273a151a0890d3 |
completed | May 3, 2026, 4:36 a.m. |
Created at: April 9, 2026, 8:58 p.m.