Triple

T26804800
Position Surface form Disambiguated ID Type / Status
Subject Berat County E671200 entity
Predicate containsAdministrativeUnit P3892 FINISHED
Object Municipality of Poliçan
The Municipality of Poliçan is a local government unit and small industrial town in south-central Albania, known historically for its weapons and munitions production.
E1742510 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: Municipality of Poliçan | Statement: [Berat County, containsAdministrativeUnit, Municipality of Poliçan]
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: Municipality of Poliçan
Triple: [Berat County, containsAdministrativeUnit, Municipality of Poliçan]
Generated description
The Municipality of Poliçan is a local government unit and small industrial town in south-central Albania, known historically for its weapons and munitions production.

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_69eeb31fbd888190a82dac5822e453bc completed April 27, 2026, 12:51 a.m.
NER Named-entity recognition batch_69f61a1b6c348190a54a0ac2a0b463b5 completed May 2, 2026, 3:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a120970126081909af25c3ee18f1f24 completed May 23, 2026, 8:09 p.m.
NEDg Description generation batch_6a120a1188e0819095663b85c750523f completed May 23, 2026, 8:12 p.m.
NED2 Entity disambiguation (via description) batch_6a120b39cddc8190a6c89274fc238b1a completed May 23, 2026, 8:16 p.m.
Created at: April 27, 2026, 4:25 a.m.