Triple
T18902463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uprooted |
E462368
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
Queen Hanna
Queen Hanna is a fictional monarch featured as a character in the work "Uprooted."
|
E1348203
|
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: Queen Hanna | Statement: [Uprooted, hasCharacter, Queen Hanna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Queen Hanna Context triple: [Uprooted, hasCharacter, Queen Hanna]
-
A.
Queen Sasha
Queen Sasha is a character from Stephen King’s fantasy novel "The Eyes of the Dragon," known as the compassionate and wise queen of Delain and mother of Prince Peter.
-
B.
Maretha
Maretha is a young girl in the television film adaptation of August Wilson's "The Piano Lesson," representing the family's next generation and their hopes for a better future.
-
C.
Melina
Melina is a key resistance fighter and love interest in the science fiction film "Total Recall," known for aiding the protagonist in his struggle against a corrupt Martian regime.
-
D.
Roxanna
Roxanna is a feminine given name of Persian origin, commonly interpreted to mean "dawn" or "bright."
-
E.
Diana
Diana is a feminine given name of Latin origin, famously borne by the Roman goddess of the hunt and by Diana, Princess of Wales.
- 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: Queen Hanna Triple: [Uprooted, hasCharacter, Queen Hanna]
Generated description
Queen Hanna is a fictional monarch featured as a character in the work "Uprooted."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Queen Hanna Target entity description: Queen Hanna is a fictional monarch featured as a character in the work "Uprooted."
-
A.
Queen Sasha
Queen Sasha is a character from Stephen King’s fantasy novel "The Eyes of the Dragon," known as the compassionate and wise queen of Delain and mother of Prince Peter.
-
B.
Maretha
Maretha is a young girl in the television film adaptation of August Wilson's "The Piano Lesson," representing the family's next generation and their hopes for a better future.
-
C.
Melina
Melina is a key resistance fighter and love interest in the science fiction film "Total Recall," known for aiding the protagonist in his struggle against a corrupt Martian regime.
-
D.
Roxanna
Roxanna is a feminine given name of Persian origin, commonly interpreted to mean "dawn" or "bright."
-
E.
Diana
Diana is a feminine given name of Latin origin, famously borne by the Roman goddess of the hunt and by Diana, Princess of Wales.
- 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_69d8dcfd05bc819088903cca13cc2846 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c52a4b4c8190b5821996e3c1741d |
completed | April 20, 2026, 6:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0582bc878c8190bfb5f058aa272225 |
completed | May 14, 2026, 8:07 a.m. |
| NEDg | Description generation | batch_6a05871baa5881908c3c0f407b2d82c0 |
completed | May 14, 2026, 8:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0587acbf1c819088bae20aa89783eb |
completed | May 14, 2026, 8:28 a.m. |
Created at: April 10, 2026, 11:58 a.m.