Triple
T9937049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lara Croft: Tomb Raider |
E193984
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Lara Croft |
E193996
|
NE FINISHED |
How this triple was built (2 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 Croft | Statement: [Lara Croft: Tomb Raider, mainCharacter, Lara Croft]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lara Croft Context triple: [Lara Croft: Tomb Raider, mainCharacter, Lara Croft]
-
A.
Lara Croft
chosen
Lara Croft is a fictional British archaeologist and adventurer, best known as the iconic protagonist of the Tomb Raider video game and film franchise.
-
B.
Lara Sanoica
Lara Sanoica is an American local politician who serves as the mayor of Rolling Meadows, Illinois.
-
C.
Lara Belmont
Lara Belmont is a British actress best known for her role in the war drama film "The War Zone."
-
D.
Lara
Lara is a semi-autobiographical novel by British writer Bernardine Evaristo that explores themes of identity, heritage, and family across generations.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca82e409348190a393777356b80a2a |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb5e4e19881909879b394090d6629 |
completed | April 2, 2026, 12:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d228e7247c81908dbba76199f2e93f |
completed | April 5, 2026, 9:18 a.m. |
Created at: March 30, 2026, 8:44 p.m.