Triple

T657489
Position Surface form Disambiguated ID Type / Status
Subject Lichtenfels E11679 entity
Predicate hasVehicleRegistrationCode P1173 FINISHED
Object LIF
LIF is the vehicle registration code for the town and district of Lichtenfels in the German state of Bavaria.
 E83252 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: LIF | Statement: [Lichtenfels, hasVehicleRegistrationCode, LIF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LIF
Context triple: [Lichtenfels, hasVehicleRegistrationCode, LIF]
  • A. LI
    LI is the Roman numeral representing the number 51.
  • B. PIF
    PIF is a regional intergovernmental organization that brings together Pacific island countries and territories to cooperate on political, economic, and security issues.
  • C. LFPB
    LFPB is the ICAO airport code for Paris–Le Bourget Airport, a historic airfield near Paris known for business aviation and the Paris Air Show.
  • D. LU
    LU is the two-letter ISO 3166-1 alpha-2 country code assigned to Luxembourg for international identification and data standards.
  • E. Rif
    Rif, also known as Rabbi Isaac Alfasi, was an 11th-century Talmudic scholar whose halachic digest of the Talmud became a foundational legal work that strongly shaped later Jewish law codes.
  • 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: LIF
Triple: [Lichtenfels, hasVehicleRegistrationCode, LIF]
Generated description
LIF is the vehicle registration code for the town and district of Lichtenfels in the German state of Bavaria.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LIF
Target entity description: LIF is the vehicle registration code for the town and district of Lichtenfels in the German state of Bavaria.
  • A. LI
    LI is the Roman numeral representing the number 51.
  • B. PIF
    PIF is a regional intergovernmental organization that brings together Pacific island countries and territories to cooperate on political, economic, and security issues.
  • C. LFPB
    LFPB is the ICAO airport code for Paris–Le Bourget Airport, a historic airfield near Paris known for business aviation and the Paris Air Show.
  • D. LU
    LU is the two-letter ISO 3166-1 alpha-2 country code assigned to Luxembourg for international identification and data standards.
  • E. Rif
    Rif, also known as Rabbi Isaac Alfasi, was an 11th-century Talmudic scholar whose halachic digest of the Talmud became a foundational legal work that strongly shaped later Jewish law codes.
  • 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_69a4932862a0819098be659c814e4981 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49fa55e048190bd9913c6c31772d0 completed March 1, 2026, 8:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5c3925a14819093336c4217c7e893 completed March 2, 2026, 5:06 p.m.
NEDg Description generation batch_69a5c423047c8190acab387bd28ffa35 completed March 2, 2026, 5:08 p.m.
NED2 Entity disambiguation (via description) batch_69a5cd1dd7848190a987276500040a4f completed March 2, 2026, 5:47 p.m.
Created at: March 1, 2026, 7:36 p.m.