Triple

T29769800
Position Surface form Disambiguated ID Type / Status
Subject Story City E755211 entity
Predicate hasHistoricObjectManufacturer P25149 FINISHED
Object Herschell-Spillman Company
The Herschell-Spillman Company was an early 20th-century American manufacturer best known for producing wooden carousels and other amusement park rides.
 E1883422 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: Herschell-Spillman Company | Statement: [Story City, hasHistoricObjectManufacturer, Herschell-Spillman Company]
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: Herschell-Spillman Company
Triple: [Story City, hasHistoricObjectManufacturer, Herschell-Spillman Company]
Generated description
The Herschell-Spillman Company was an early 20th-century American manufacturer best known for producing wooden carousels and other amusement park rides.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: hasHistoricObjectManufacturer
Context triple: [Story City, hasHistoricObjectManufacturer, Herschell-Spillman Company]
  • A. hasManufacturerHistory
    Indicates that there exists a record of past and/or current manufacturers associated with an entity, capturing changes or continuity in who produced it over time.
  • B. hasProductionHistory
    Indicates that an entity is associated with a record or account of its past production activities, processes, or outputs.
  • C. formerManufacturer chosen
    Indicates that an entity previously manufactured another entity but no longer does so.
  • D. hasHistoricalEntity
    Indicates a relationship where one entity includes, references, or is associated with another entity that existed or is defined in a past historical context.
  • E. historicalBrandOwner
    Indicates that one entity was a past (but not current) owner of a particular brand.
  • 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_69f0ef878574819088c867fd1a5c8b86 completed April 28, 2026, 5:33 p.m.
NER Named-entity recognition batch_69fd6f9d600c8190acf495b7fc632e4b completed May 8, 2026, 5:07 a.m.
NED1 Entity disambiguation (via context triple) batch_6a26c8f580ec819095b3ad8647757e96 completed June 8, 2026, 1:51 p.m.
NEDg Description generation batch_6a26cce42b208190b30d28fe1e55105b completed June 8, 2026, 2:08 p.m.
NED2 Entity disambiguation (via description) batch_6a26d421b5408190bfb77e624443fad8 completed June 8, 2026, 2:39 p.m.
PD Predicate disambiguation batch_69fd6e98a2948190a9f78c415ad23b8c completed May 8, 2026, 5:03 a.m.
Created at: April 28, 2026, 8:41 p.m.