Triple
T9781369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arrowverse |
E237380
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object |
Kara Danvers
Kara Danvers is the civilian identity of Supergirl, a Kryptonian superhero and central protagonist in the Arrowverse television franchise.
|
E820684
|
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: Kara Danvers | Statement: [Arrowverse, hasMainCharacter, Kara Danvers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kara Danvers Context triple: [Arrowverse, hasMainCharacter, Kara Danvers]
-
A.
Valeria Richards
Valeria Richards is a highly intelligent Marvel Comics character, the daughter of Reed Richards and Sue Storm, often depicted as a child prodigy whose genius rivals that of her father.
-
B.
Jennifer Walters
Jennifer Walters is a Marvel Comics lawyer who becomes the superhero She-Hulk after receiving a blood transfusion from her cousin Bruce Banner.
-
C.
Jo Grant
Jo Grant is a spirited and resourceful companion of the Third Doctor in the classic British science fiction series Doctor Who.
-
D.
Martha Coleman
Martha Coleman is a film producer known for her work on the British comedy-drama "Praise."
-
E.
Gwen Tyler
Gwen Tyler is a fictional character featured in a toy line, likely designed as part of a themed set or narrative-driven collection.
- 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: Kara Danvers Triple: [Arrowverse, hasMainCharacter, Kara Danvers]
Generated description
Kara Danvers is the civilian identity of Supergirl, a Kryptonian superhero and central protagonist in the Arrowverse television franchise.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kara Danvers Target entity description: Kara Danvers is the civilian identity of Supergirl, a Kryptonian superhero and central protagonist in the Arrowverse television franchise.
-
A.
Valeria Richards
Valeria Richards is a highly intelligent Marvel Comics character, the daughter of Reed Richards and Sue Storm, often depicted as a child prodigy whose genius rivals that of her father.
-
B.
Jennifer Walters
Jennifer Walters is a Marvel Comics lawyer who becomes the superhero She-Hulk after receiving a blood transfusion from her cousin Bruce Banner.
-
C.
Jo Grant
Jo Grant is a spirited and resourceful companion of the Third Doctor in the classic British science fiction series Doctor Who.
-
D.
Martha Coleman
Martha Coleman is a film producer known for her work on the British comedy-drama "Praise."
-
E.
Gwen Tyler
Gwen Tyler is a fictional character featured in a toy line, likely designed as part of a themed set or narrative-driven collection.
- 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_69ca84da927881909bda80caecad6010 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda1b23cb88190b458ab18d5f7f493 |
completed | April 1, 2026, 10:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1bd2e5f4c81908a3c132df6440947 |
completed | April 5, 2026, 1:38 a.m. |
| NEDg | Description generation | batch_69d1bf4a414c81909ee12092315e714a |
completed | April 5, 2026, 1:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1c00dac1c81908c6c5dca384eddcc |
completed | April 5, 2026, 1:51 a.m. |
Created at: March 30, 2026, 8:27 p.m.