Triple

T12175854
Position Surface form Disambiguated ID Type / Status
Subject Herbig–Haro object E290083 entity
Predicate cataloguedAs P5301 FINISHED
Object HH 2
HH 2 is a well-known Herbig–Haro object consisting of bright nebulous knots formed by jets from a young star colliding with surrounding interstellar gas.
 E970249 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: HH 2 | Statement: [Herbig–Haro object, cataloguedAs, HH 2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: HH 2
Context triple: [Herbig–Haro object, cataloguedAs, HH 2]
  • A. HH 1
    HH 1 is a well-known Herbig–Haro object, a small, bright patch of nebulosity formed by jets from a newborn star colliding with surrounding interstellar gas and dust.
  • B. HH
    HH is the vehicle registration code used on license plates for the German city-state of Hamburg.
  • C. HH 111
    HH 111 is a well-studied Herbig–Haro jet in the Orion region, notable for its highly collimated bipolar outflows from a young stellar object.
  • D. HH 34
    HH 34 is a well-studied Herbig–Haro object consisting of bright emission knots and jets produced by a young star interacting with the surrounding interstellar medium.
  • E. HII
    HII is a major American defense contractor and shipbuilding company best known for constructing U.S. Navy aircraft carriers and other military vessels.
  • 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: HH 2
Triple: [Herbig–Haro object, cataloguedAs, HH 2]
Generated description
HH 2 is a well-known Herbig–Haro object consisting of bright nebulous knots formed by jets from a young star colliding with surrounding interstellar gas.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: HH 2
Target entity description: HH 2 is a well-known Herbig–Haro object consisting of bright nebulous knots formed by jets from a young star colliding with surrounding interstellar gas.
  • A. HH 1
    HH 1 is a well-known Herbig–Haro object, a small, bright patch of nebulosity formed by jets from a newborn star colliding with surrounding interstellar gas and dust.
  • B. HH
    HH is the vehicle registration code used on license plates for the German city-state of Hamburg.
  • C. HH 111
    HH 111 is a well-studied Herbig–Haro jet in the Orion region, notable for its highly collimated bipolar outflows from a young stellar object.
  • D. HH 34
    HH 34 is a well-studied Herbig–Haro object consisting of bright emission knots and jets produced by a young star interacting with the surrounding interstellar medium.
  • E. HII
    HII is a major American defense contractor and shipbuilding company best known for constructing U.S. Navy aircraft carriers and other military vessels.
  • 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_69d6ab4d6c00819095a9a7c35de83cfb completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d915dc71788190bdaadf7be9d8d6ce completed April 10, 2026, 3:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69f60a88b8748190a6c95e143f370010 completed May 2, 2026, 2:30 p.m.
NEDg Description generation batch_69f60bdb39f48190ad6bc51db6c34163 completed May 2, 2026, 2:36 p.m.
NED2 Entity disambiguation (via description) batch_69f60d4889d48190b2dfda8a0978cc72 completed May 2, 2026, 2:42 p.m.
Created at: April 8, 2026, 9:50 p.m.