Triple

T34838407
Position Surface form Disambiguated ID Type / Status
Subject Ludlow Mills complex E1004265 entity
Predicate developedBy P73 FINISHED
Object Ludlow Manufacturing Associates
Ludlow Manufacturing Associates was a prominent industrial company known for operating large textile mills and shaping the economic development of Ludlow, Massachusetts.
E2114474 NE FINISHED

How this triple was built (2 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: Ludlow Manufacturing Associates | Statement: [Ludlow Mills complex, developedBy, Ludlow Manufacturing Associates]
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: Ludlow Manufacturing Associates
Triple: [Ludlow Mills complex, developedBy, Ludlow Manufacturing Associates]
Generated description
Ludlow Manufacturing Associates was a prominent industrial company known for operating large textile mills and shaping the economic development of Ludlow, Massachusetts.

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_69f76db97714819099b5bed36fd64e9d completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f7812cd1cc819083c3c02c338d6a7d completed May 3, 2026, 5:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37794c6684819090dfd1e890662c0a completed June 21, 2026, 5:40 a.m.
NEDg Description generation batch_6a3779d574e481909c73b8be299eae8f completed June 21, 2026, 5:42 a.m.
NED2 Entity disambiguation (via description) batch_6a377a7836bc8190a77adab5c1c04df7 completed June 21, 2026, 5:45 a.m.
Created at: May 3, 2026, 4 p.m.