Triple

T6010112
Position Surface form Disambiguated ID Type / Status
Subject T2K E133809 entity
Predicate nearDetectorType P7243 FINISHED
Object INGRID
INGRID is a near detector of the T2K long-baseline neutrino experiment, designed to monitor the neutrino beam’s direction and intensity.
 E561155 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: INGRID | Statement: [T2K, nearDetectorType, INGRID]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: INGRID
Context triple: [T2K, nearDetectorType, INGRID]
  • A. Ingrid
    Ingrid is a feminine given name of Scandinavian origin that has been borne by several notable figures, including the Swedish actress Ingrid Bergman.
  • B. Ingeborg
    Ingeborg is a feminine given name of Germanic origin, commonly used in German-speaking and Scandinavian countries.
  • C. Inge
    Inge is a given name of Germanic origin used in various European countries for both males and females.
  • D. Arabella
    Arabella is a feminine given name of Latin origin, often associated with elegance and used in various English-speaking cultures.
  • E. Arabella
    Arabella is a romantic opera in three acts by Richard Strauss, first performed in 1933, known for its lush orchestration and exploration of love and social expectations in 19th-century Vienna.
  • 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: INGRID
Triple: [T2K, nearDetectorType, INGRID]
Generated description
INGRID is a near detector of the T2K long-baseline neutrino experiment, designed to monitor the neutrino beam’s direction and intensity.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: INGRID
Target entity description: INGRID is a near detector of the T2K long-baseline neutrino experiment, designed to monitor the neutrino beam’s direction and intensity.
  • A. Ingrid
    Ingrid is a feminine given name of Scandinavian origin that has been borne by several notable figures, including the Swedish actress Ingrid Bergman.
  • B. Ingeborg
    Ingeborg is a feminine given name of Germanic origin, commonly used in German-speaking and Scandinavian countries.
  • C. Inge
    Inge is a given name of Germanic origin used in various European countries for both males and females.
  • D. Arabella
    Arabella is a feminine given name of Latin origin, often associated with elegance and used in various English-speaking cultures.
  • E. Arabella
    Arabella is a romantic opera in three acts by Richard Strauss, first performed in 1933, known for its lush orchestration and exploration of love and social expectations in 19th-century Vienna.
  • 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_69c0087361a48190905c6b55969852b8 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c0560bae148190ad4755defaaf471b completed March 22, 2026, 8:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69c108a17bc88190b710a1858120a32d completed March 23, 2026, 9:32 a.m.
NEDg Description generation batch_69c1099f00f88190a5f1f0fafbb679c2 completed March 23, 2026, 9:36 a.m.
NED2 Entity disambiguation (via description) batch_69c10a2ffdcc8190bfeebc59d98b2b29 completed March 23, 2026, 9:38 a.m.
Created at: March 22, 2026, 4:06 p.m.