Triple

T3080786
Position Surface form Disambiguated ID Type / Status
Subject Poncol railway station E64250 entity
Predicate connectsTo P845 FINISHED
Object Tegal E372519 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: Tegal | Statement: [Poncol railway station, connectsTo, Tegal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tegal
Context triple: [Poncol railway station, connectsTo, Tegal]
  • A. Tegal chosen
    Tegal is a coastal city in Central Java, Indonesia, known as a regional transport hub and trading center on the north coast railway line.
  • B. Semarang
    Semarang is a major coastal city on the north coast of Java in Indonesia, known historically as an important colonial trading hub and now as a significant commercial and industrial center.
  • C. Pekalongan
    Pekalongan is an Indonesian coastal city on the island of Java renowned as a major center of batik production and textile arts.
  • D. Purwokerto
    Purwokerto is a major town in Central Java, Indonesia, known as a regional economic and educational center and a gateway to nearby highland tourist destinations.
  • E. Cirebon
    Cirebon is a coastal city in West Java, Indonesia, known as a cultural crossroads blending Sundanese and Javanese influences and serving as a significant regional trading and urban center.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ad857bb4c88190a4cf27893fcabed8 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada1aaf6d48190af4f9106965589b0 completed March 8, 2026, 4:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4f0130db48190b6662c8dabf67d1d completed March 14, 2026, 5:20 a.m.
Created at: March 8, 2026, 3:03 p.m.