Triple
T5008230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bernardine Evaristo |
E112548
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Lara
Lara is a semi-autobiographical novel by British writer Bernardine Evaristo that explores themes of identity, heritage, and family across generations.
|
E487107
|
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: Lara | Statement: [Bernardine Evaristo, notableWork, Lara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lara Context triple: [Bernardine Evaristo, notableWork, Lara]
-
A.
Lara
Lara is a feminine given name, often used in various cultures and languages, sometimes as a variant of Laura or derived from Latin and Russian origins.
-
B.
Lara Sanoica
Lara Sanoica is an American local politician who serves as the mayor of Rolling Meadows, Illinois.
-
C.
Lara, Victoria
Lara, Victoria is a regional township in the City of Greater Geelong, Australia, known as a growing commuter suburb between Melbourne and Geelong.
-
D.
Lara Croft
Lara Croft is a fictional British archaeologist and adventurer, best known as the iconic protagonist of the Tomb Raider video game and film franchise.
-
E.
Lara Breay
Lara Breay is a film producer best known for her work on the animated superhero comedy "Megamind."
- 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: Lara Triple: [Bernardine Evaristo, notableWork, Lara]
Generated description
Lara is a semi-autobiographical novel by British writer Bernardine Evaristo that explores themes of identity, heritage, and family across generations.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lara Target entity description: Lara is a semi-autobiographical novel by British writer Bernardine Evaristo that explores themes of identity, heritage, and family across generations.
-
A.
Lara
Lara is a feminine given name, often used in various cultures and languages, sometimes as a variant of Laura or derived from Latin and Russian origins.
-
B.
Lara Sanoica
Lara Sanoica is an American local politician who serves as the mayor of Rolling Meadows, Illinois.
-
C.
Lara, Victoria
Lara, Victoria is a regional township in the City of Greater Geelong, Australia, known as a growing commuter suburb between Melbourne and Geelong.
-
D.
Lara Croft
Lara Croft is a fictional British archaeologist and adventurer, best known as the iconic protagonist of the Tomb Raider video game and film franchise.
-
E.
Lara Breay
Lara Breay is a film producer best known for her work on the animated superhero comedy "Megamind."
- 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_69bd4433d0b08190877e83959ef40d81 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd72eb05f881908d7dc3d7cd07b2ae |
completed | March 20, 2026, 4:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be9266314881909dfb710c5b5c8f65 |
completed | March 21, 2026, 12:43 p.m. |
| NEDg | Description generation | batch_69be93cfbe2881908cf97dd9ec5d28b5 |
completed | March 21, 2026, 12:49 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69be9448b5148190903775d394a095f0 |
completed | March 21, 2026, 12:51 p.m. |
Created at: March 20, 2026, 1:35 p.m.