Triple

T1738368
Position Surface form Disambiguated ID Type / Status
Subject Tara Lipinski E37971 entity
Predicate givenName P17 FINISHED
Object Tara
Tara is a female given name commonly used in English-speaking countries, often associated with notable figures such as Olympic figure skater Tara Lipinski.
 E193416 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: Tara | Statement: [Tara Lipinski, givenName, Tara]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tara
Context triple: [Tara Lipinski, givenName, Tara]
  • A. Teressa
    Teressa is a Nicobarese language variety spoken by the indigenous community on Teressa Island in India’s Nicobar archipelago.
  • B. Shira
    Shira is the eroded western volcanic cone and plateau of Mount Kilimanjaro, forming one of the mountain’s three main summits.
  • C. Loralai
    Loralai is a town and district in northern Balochistan, Pakistan, known historically as a regional administrative and trade center.
  • D. Dara
    Dara is a given name most prominently associated with Dara Khosrowshahi, the Iranian-American businessman and CEO of Uber.
  • E. Tain
    Tain is a historic town in the Highlands of Scotland, known as one of the country’s oldest royal burghs and a regional administrative and judicial center.
  • 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: Tara
Triple: [Tara Lipinski, givenName, Tara]
Generated description
Tara is a female given name commonly used in English-speaking countries, often associated with notable figures such as Olympic figure skater Tara Lipinski.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tara
Target entity description: Tara is a female given name commonly used in English-speaking countries, often associated with notable figures such as Olympic figure skater Tara Lipinski.
  • A. Teressa
    Teressa is a Nicobarese language variety spoken by the indigenous community on Teressa Island in India’s Nicobar archipelago.
  • B. Shira
    Shira is the eroded western volcanic cone and plateau of Mount Kilimanjaro, forming one of the mountain’s three main summits.
  • C. Loralai
    Loralai is a town and district in northern Balochistan, Pakistan, known historically as a regional administrative and trade center.
  • D. Dara
    Dara is a given name most prominently associated with Dara Khosrowshahi, the Iranian-American businessman and CEO of Uber.
  • E. Tain
    Tain is a historic town in the Highlands of Scotland, known as one of the country’s oldest royal burghs and a regional administrative and judicial center.
  • 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_69a8861cc6ac8190ac0b2e31ccf62851 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa63c35aec8190b5c19ace5524173f completed March 6, 2026, 5:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad8b03303c8190a301dca327bf9f47 completed March 8, 2026, 2:43 p.m.
NEDg Description generation batch_69ad957e9a6c81909d52bf2def797526 completed March 8, 2026, 3:27 p.m.
NED2 Entity disambiguation (via description) batch_69ad97b6c03881909f278594e800c0f5 completed March 8, 2026, 3:37 p.m.
Created at: March 4, 2026, 7:30 p.m.