Triple

T2965291
Position Surface form Disambiguated ID Type / Status
Subject Girona E80146 entity
Predicate locatedOnRiver P165 FINISHED
Object Ter
The Ter is a river in northeastern Catalonia, Spain, that flows through cities such as Girona before emptying into the Mediterranean Sea.
 E315238 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: Ter | Statement: [Girona, locatedOnRiver, Ter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ter
Context triple: [Girona, locatedOnRiver, Ter]
  • A. TER
    TER is a network of regional express trains in France that provides local passenger rail services across various regions.
  • B. Tur
    Tur is a foundational 14th-century Jewish legal code by Rabbi Jacob ben Asher that systematically organized halakhic rulings and served as a primary basis for later works like the Shulchan Aruch.
  • C. tet
    tet is the ISO 639-1 language code for Tetum, an Austronesian language spoken primarily in East Timor.
  • D. TR
    TR is the two-letter ISO 3166-1 alpha-2 country code assigned to Turkey for international standardization and referencing.
  • E. Tek
    Tek is a brand associated with Tektronix, known for electronic test and measurement equipment such as oscilloscopes and signal analyzers.
  • 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: Ter
Triple: [Girona, locatedOnRiver, Ter]
Generated description
The Ter is a river in northeastern Catalonia, Spain, that flows through cities such as Girona before emptying into the Mediterranean Sea.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ter
Target entity description: The Ter is a river in northeastern Catalonia, Spain, that flows through cities such as Girona before emptying into the Mediterranean Sea.
  • A. TER
    TER is a network of regional express trains in France that provides local passenger rail services across various regions.
  • B. Tur
    Tur is a foundational 14th-century Jewish legal code by Rabbi Jacob ben Asher that systematically organized halakhic rulings and served as a primary basis for later works like the Shulchan Aruch.
  • C. tet
    tet is the ISO 639-1 language code for Tetum, an Austronesian language spoken primarily in East Timor.
  • D. TR
    TR is the two-letter ISO 3166-1 alpha-2 country code assigned to Turkey for international standardization and referencing.
  • E. Tek
    Tek is a brand associated with Tektronix, known for electronic test and measurement equipment such as oscilloscopes and signal analyzers.
  • 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_69ad8b1341848190bd19dbf46892887d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad995a28e88190a4d6b9ef2c0d8e61 completed March 8, 2026, 3:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69b0fc9bc190819087cb35ee7c78825a completed March 11, 2026, 5:24 a.m.
NEDg Description generation batch_69b0fd25e07c819088b2b1bcef4cf54e completed March 11, 2026, 5:27 a.m.
NED2 Entity disambiguation (via description) batch_69b100ecbee081908832ddec0efdc751 completed March 11, 2026, 5:43 a.m.
Created at: March 8, 2026, 2:58 p.m.