Triple

T675665
Position Surface form Disambiguated ID Type / Status
Subject Orly 4 E13071 entity
Predicate formerName P65 FINISHED
Object Orly Sud
Orly Sud is the former name of Orly 4, a terminal facility at Paris Orly Airport in France.
 E86080 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: Orly Sud | Statement: [Orly 4, formerName, Orly Sud]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Orly Sud
Context triple: [Orly 4, formerName, Orly Sud]
  • A. Rachel Cohen-Kagan
    Rachel Cohen-Kagan was an Israeli politician, women's rights activist, and one of the signatories of Israel's Declaration of Independence.
  • B. Einat Kalisch-Rotem
    Einat Kalisch-Rotem is an Israeli urban planner and politician who became the first female mayor of Haifa.
  • C. Tamara Geva
    Tamara Geva was a Russian-American dancer and actress known for her early collaborations with choreographer George Balanchine and her influential work on Broadway and in modern ballet.
  • D. Irin Carmon
    Irin Carmon is a journalist and author best known for co-writing the biography "Notorious RBG" about Supreme Court Justice Ruth Bader Ginsburg.
  • E. Debra Lerner Cohen
    Debra Lerner Cohen is a member of the prominent Lerner family, known for its significant influence in American real estate and professional sports ownership.
  • 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: Orly Sud
Triple: [Orly 4, formerName, Orly Sud]
Generated description
Orly Sud is the former name of Orly 4, a terminal facility at Paris Orly Airport in France.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Orly Sud
Target entity description: Orly Sud is the former name of Orly 4, a terminal facility at Paris Orly Airport in France.
  • A. Rachel Cohen-Kagan
    Rachel Cohen-Kagan was an Israeli politician, women's rights activist, and one of the signatories of Israel's Declaration of Independence.
  • B. Einat Kalisch-Rotem
    Einat Kalisch-Rotem is an Israeli urban planner and politician who became the first female mayor of Haifa.
  • C. Tamara Geva
    Tamara Geva was a Russian-American dancer and actress known for her early collaborations with choreographer George Balanchine and her influential work on Broadway and in modern ballet.
  • D. Irin Carmon
    Irin Carmon is a journalist and author best known for co-writing the biography "Notorious RBG" about Supreme Court Justice Ruth Bader Ginsburg.
  • E. Debra Lerner Cohen
    Debra Lerner Cohen is a member of the prominent Lerner family, known for its significant influence in American real estate and professional sports ownership.
  • 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_69a4933d3bf88190972041cd8cf143b9 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a4a0266e7c8190a94c4b4b761c59f4 completed March 1, 2026, 8:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69a6374cc0d48190900e96a374ce35af completed March 3, 2026, 1:20 a.m.
NEDg Description generation batch_69a637b4c578819086dd60ee6224ceef completed March 3, 2026, 1:21 a.m.
NED2 Entity disambiguation (via description) batch_69a63860ec4c8190919115af097b9bfa completed March 3, 2026, 1:24 a.m.
Created at: March 1, 2026, 7:36 p.m.