Triple

T15204981
Position Surface form Disambiguated ID Type / Status
Subject Lana Condor E363365 entity
Predicate givenName P17 FINISHED
Object Lana
Lana is the given name of actress Lana Condor, best known for starring in the "To All the Boys I've Loved Before" film series.
 E1144990 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: Lana | Statement: [Lana Condor, givenName, Lana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lana
Context triple: [Lana Condor, givenName, Lana]
  • A. Lana
    Lana is a filmmaker best known as one of the Wachowski sisters, the co-creators of The Matrix film series.
  • B. Lana
    Lana is a river in Albania that flows through the capital city of Tirana before joining the Tirana River.
  • C. Lana
    Lana is the ISO 15924 four-letter code used to represent the Tai Tham script in international standards.
  • D. Lana
    Lana is a professional wrestler and television personality best known for her time in WWE, where she appeared prominently as a manager and in-ring performer.
  • E. Lana
    Lana is the seductive call girl who becomes the central love interest and catalyst for chaos in the 1983 film "Risky Business."
  • 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: Lana
Triple: [Lana Condor, givenName, Lana]
Generated description
Lana is the given name of actress Lana Condor, best known for starring in the "To All the Boys I've Loved Before" film series.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lana
Target entity description: Lana is the given name of actress Lana Condor, best known for starring in the "To All the Boys I've Loved Before" film series.
  • A. Lana
    Lana is the seductive call girl who becomes the central love interest and catalyst for chaos in the 1983 film "Risky Business."
  • B. Lana
    Lana is a filmmaker best known as one of the Wachowski sisters, the co-creators of The Matrix film series.
  • C. Lana
    Lana is a river in Albania that flows through the capital city of Tirana before joining the Tirana River.
  • D. Lana
    Lana is the ISO 15924 four-letter code used to represent the Tai Tham script in international standards.
  • E. Lana
    Lana is a professional wrestler and television personality best known for her time in WWE, where she appeared prominently as a manager and in-ring performer.
  • 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e006b7964c8190bc8dc3444b94f15e completed April 15, 2026, 9:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69fedd2dc6f08190a8f1612ac29a8654 completed May 9, 2026, 7:07 a.m.
NEDg Description generation batch_69fedf49f1c88190857cb1555e98c97e completed May 9, 2026, 7:16 a.m.
NED2 Entity disambiguation (via description) batch_69fee06a90448190b7733aa5ee8a5d62 completed May 9, 2026, 7:21 a.m.
Created at: April 10, 2026, 3:11 a.m.