Triple

T23131754
Position Surface form Disambiguated ID Type / Status
Subject A234 road E577186 entity
Predicate connects P390 FINISHED
Object Penge NE NERFINISHED

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: Penge | Statement: [A234 road, connects, Penge]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Penge
Context triple: [A234 road, connects, Penge]
  • A. Penge chosen
    Penge is a suburban district in southeast London known for its Victorian architecture and proximity to Crystal Palace.
  • B. Pengo
    Pengo is a Dravidian language spoken primarily by the Pengo people in parts of central India, especially in Odisha and neighboring regions.
  • C. Penkun
    Penkun is a small historic town in northeastern Germany, located near the Polish border in the state of Mecklenburg-Vorpommern.
  • D. Pangim
    Pangim, also known as Panaji, is the riverside city that serves as the administrative and cultural center of the Indian state of Goa.
  • E. Peng
    Peng is a Chinese surname borne by numerous notable figures in politics, arts, and academia throughout Chinese history and the modern era.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e245f7b0e481909c473ff4e6a54e2c completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f18e88909881908c695cd7d39d380c completed April 29, 2026, 4:52 a.m.
Created at: April 17, 2026, 4 p.m.