Triple
T19918271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moxyland |
E478720
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Kendra
Kendra is a central protagonist in Lauren Beukes' dystopian novel "Moxyland," depicted as an ambitious art-school dropout turned corporate-sponsored viral marketer navigating a high-tech, authoritarian Cape Town.
|
E1402104
|
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: Kendra | Statement: [Moxyland, mainCharacter, Kendra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kendra Context triple: [Moxyland, mainCharacter, Kendra]
-
A.
Kendra
Kendra is a character from the Harry Potter series, known as Albus Dumbledore’s mother.
-
B.
Kendra
Kendra is a reality television series created by producer Kevin Burns that follows the life of former Playboy model Kendra Wilkinson.
-
C.
Kendall
Kendall is a unisex given name of English origin commonly used in the United States and other English-speaking countries.
-
D.
Kendall
Kendall is a neighborhood in Cambridge, Massachusetts, known for its proximity to MIT and its concentration of technology companies and research institutions.
-
E.
Kendall
Kendall is a fictional character portrayed by American actor Terry O'Quinn, best known for his roles in television dramas and thrillers.
- 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: Kendra Triple: [Moxyland, mainCharacter, Kendra]
Generated description
Kendra is a central protagonist in Lauren Beukes' dystopian novel "Moxyland," depicted as an ambitious art-school dropout turned corporate-sponsored viral marketer navigating a high-tech, authoritarian Cape Town.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kendra Target entity description: Kendra is a central protagonist in Lauren Beukes' dystopian novel "Moxyland," depicted as an ambitious art-school dropout turned corporate-sponsored viral marketer navigating a high-tech, authoritarian Cape Town.
-
A.
Kendra
Kendra is a character from the Harry Potter series, known as Albus Dumbledore’s mother.
-
B.
Kendra
Kendra is a reality television series created by producer Kevin Burns that follows the life of former Playboy model Kendra Wilkinson.
-
C.
Kendall
Kendall is a unisex given name of English origin commonly used in the United States and other English-speaking countries.
-
D.
Kendall
Kendall is a neighborhood in Cambridge, Massachusetts, known for its proximity to MIT and its concentration of technology companies and research institutions.
-
E.
Kendall
Kendall is a fictional character portrayed by American actor Terry O'Quinn, best known for his roles in television dramas and thrillers.
- 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_69d8e521855c8190b41871700afc8d6a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e659c3d990819089386d9f30323e8c |
completed | April 20, 2026, 4:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07ea52592481909419476f335b6046 |
completed | May 16, 2026, 3:53 a.m. |
| NEDg | Description generation | batch_6a07f2c7dec48190a698509098479a56 |
completed | May 16, 2026, 4:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07f376f5388190989a0460528945df |
completed | May 16, 2026, 4:32 a.m. |
Created at: April 10, 2026, 1:53 p.m.