Triple

T22559938
Position Surface form Disambiguated ID Type / Status
Subject Michele di Lando E557784 entity
Predicate familyName P18 FINISHED
Object di Lando
di Lando is an Italian surname historically associated with figures such as Michele di Lando, a notable 14th-century Florentine leader.
 E1543439 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: di Lando | Statement: [Michele di Lando, familyName, di Lando]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: di Lando
Context triple: [Michele di Lando, familyName, di Lando]
  • A. Landy
    Landy is the official mascot character of the South Korean professional baseball team SSG Landers.
  • B. Landin
    Landin is a surname most notably associated with Peter Landin, a pioneering British computer scientist in the field of programming language theory.
  • C. Dodi
    Dodi was an Egyptian film producer and the romantic partner of Diana, Princess of Wales, who died alongside her in a 1997 car crash in Paris.
  • D. Fusso
    Fusso is one of Casper the Friendly Ghost’s lesser-known ghost uncles from the Casper franchise.
  • E. Lansen
    Lansen is the NATO reporting name for the Swedish Saab 32, a Cold War-era jet aircraft used primarily for attack and reconnaissance roles.
  • 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: di Lando
Triple: [Michele di Lando, familyName, di Lando]
Generated description
di Lando is an Italian surname historically associated with figures such as Michele di Lando, a notable 14th-century Florentine leader.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: di Lando
Target entity description: di Lando is an Italian surname historically associated with figures such as Michele di Lando, a notable 14th-century Florentine leader.
  • A. Landy
    Landy is the official mascot character of the South Korean professional baseball team SSG Landers.
  • B. Landin
    Landin is a surname most notably associated with Peter Landin, a pioneering British computer scientist in the field of programming language theory.
  • C. Dodi
    Dodi was an Egyptian film producer and the romantic partner of Diana, Princess of Wales, who died alongside her in a 1997 car crash in Paris.
  • D. Fusso
    Fusso is one of Casper the Friendly Ghost’s lesser-known ghost uncles from the Casper franchise.
  • E. Lansen
    Lansen is the NATO reporting name for the Swedish Saab 32, a Cold War-era jet aircraft used primarily for attack and reconnaissance roles.
  • 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_69e11e59db848190b4272ecd2b690ffd completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15f7c914881909584c46ae323c779 completed April 29, 2026, 1:31 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0b2d6a72448190bddf2e280b6fca76 completed May 18, 2026, 3:16 p.m.
NEDg Description generation batch_6a0b364718308190937c3b7ae90ea9df completed May 18, 2026, 3:54 p.m.
NED2 Entity disambiguation (via description) batch_6a0b37a1ecc08190ac89e862582833a0 completed May 18, 2026, 4 p.m.
Created at: April 16, 2026, 8:52 p.m.