Triple

T26495485
Position Surface form Disambiguated ID Type / Status
Subject Hisingen E669270 entity
Predicate hasShipyardHistory P4334 FINISHED
Object Götaverken shipyard
Götaverken shipyard was one of Sweden’s major historic shipbuilding companies, based in Gothenburg and known for constructing a wide range of commercial and naval vessels.
 E1728260 NE FINISHED

How this triple was built (3 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: Götaverken shipyard | Statement: [Hisingen, hasShipyardHistory, Götaverken shipyard]
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: Götaverken shipyard
Triple: [Hisingen, hasShipyardHistory, Götaverken shipyard]
Generated description
Götaverken shipyard was one of Sweden’s major historic shipbuilding companies, based in Gothenburg and known for constructing a wide range of commercial and naval vessels.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: hasShipyardHistory
Context triple: [Hisingen, hasShipyardHistory, Götaverken shipyard]
  • A. shipyardFormerName
    Indicates that a shipyard previously operated under a different name, specifying what that former name was.
  • B. hasShipyardType
    Indicates the specific category or classification of shipyard associated with an entity.
  • C. hasHistoricVesselsFrom
    Indicates that an entity possesses or includes historic vessels that originate from a specified place or source.
  • D. shipyardLocatedOn
    Indicates that a shipyard is situated on or within the geographic area of a specified landmass, coastline, or body of water.
  • E. shipyard chosen
    Indicates a relationship where a location functions as a facility for building, repairing, or maintaining ships.
  • F. None of above.

Provenance (6 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_69eeb319007081909642b414b114b35a completed April 27, 2026, 12:51 a.m.
NER Named-entity recognition batch_69f65f7731e4819099d5bd3d915ee266 completed May 2, 2026, 8:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11bb2f3e808190bc0d6926c6b933e8 completed May 23, 2026, 2:35 p.m.
NEDg Description generation batch_6a11be60f78c819093363b32bd4e3447 completed May 23, 2026, 2:49 p.m.
NED2 Entity disambiguation (via description) batch_6a11bf9449b08190bcaff036e81d9392 completed May 23, 2026, 2:54 p.m.
PD Predicate disambiguation batch_69f65c1f94ac8190bc6fbc7916fc0d82 completed May 2, 2026, 8:18 p.m.
Created at: April 27, 2026, 1:08 a.m.