Triple

T10445356
Position Surface form Disambiguated ID Type / Status
Subject province of Östergötland E246271 entity
Predicate hasTown P847 FINISHED
Object Vadstena
Vadstena is a historic Swedish town known for its medieval castle, monastery, and well-preserved old town on the shores of Lake Vättern.
 E864237 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: Vadstena | Statement: [province of Östergötland, hasTown, Vadstena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vadstena
Context triple: [province of Östergötland, hasTown, Vadstena]
  • A. Siljan
    Siljan is a lake in Telemark, Norway, known for its scenic surroundings and proximity to the town of Skien.
  • B. Råðdjärum
    Råðdjärum is the Elfdalian-language name for the Elfdalian Language Council, an organization dedicated to preserving and promoting the Elfdalian language.
  • C. Namsskogan
    Namsskogan is a sparsely populated inland municipality in Trøndelag county, Norway, known for its vast forests, wildlife, and outdoor recreation opportunities.
  • D. Arvidsjaur
    Arvidsjaur is a small town in northern Sweden known for its military presence, winter testing facilities, and proximity to Arctic wilderness.
  • E. Skarpäng
    Skarpäng is a residential urban area within Täby Municipality in Stockholm County, Sweden.
  • 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: Vadstena
Triple: [province of Östergötland, hasTown, Vadstena]
Generated description
Vadstena is a historic Swedish town known for its medieval castle, monastery, and well-preserved old town on the shores of Lake Vättern.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vadstena
Target entity description: Vadstena is a historic Swedish town known for its medieval castle, monastery, and well-preserved old town on the shores of Lake Vättern.
  • A. Siljan
    Siljan is a lake in Telemark, Norway, known for its scenic surroundings and proximity to the town of Skien.
  • B. Råðdjärum
    Råðdjärum is the Elfdalian-language name for the Elfdalian Language Council, an organization dedicated to preserving and promoting the Elfdalian language.
  • C. Namsskogan
    Namsskogan is a sparsely populated inland municipality in Trøndelag county, Norway, known for its vast forests, wildlife, and outdoor recreation opportunities.
  • D. Arvidsjaur
    Arvidsjaur is a small town in northern Sweden known for its military presence, winter testing facilities, and proximity to Arctic wilderness.
  • E. Skarpäng
    Skarpäng is a residential urban area within Täby Municipality in Stockholm County, Sweden.
  • 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_69d381c04fe08190957c26c526a3b05a completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4fdbf81508190a160edea85105d3a completed April 7, 2026, 12:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69d87eeae8788190b63534fa4f942ead completed April 10, 2026, 4:39 a.m.
NEDg Description generation batch_69d886c3fdcc8190a67a7f7788b8a2e8 completed April 10, 2026, 5:12 a.m.
NED2 Entity disambiguation (via description) batch_69d88dc15ab481909011c5de93bbab14 completed April 10, 2026, 5:42 a.m.
Created at: April 6, 2026, 12:16 p.m.