Triple

T24319497
Position Surface form Disambiguated ID Type / Status
Subject Tarłów E612917 entity
Predicate hasCounty P285 FINISHED
Object Ostrowiec
Ostrowiec is a county in south-central Poland, known for its administrative center Ostrowiec Świętokrzyski in the Świętokrzyskie Voivodeship.
E206717 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: Ostrowiec | Statement: [Tarłów, hasCounty, Ostrowiec]
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: Ostrowiec
Triple: [Tarłów, hasCounty, Ostrowiec]
Generated description
Ostrowiec is a county in south-central Poland, known for its administrative center Ostrowiec Świętokrzyski in the Świętokrzyskie Voivodeship.

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_69e2d7da491c8190b6e6218af50923db completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f292aa63fc8190a874367c9010f283 completed April 29, 2026, 11:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3d177501388190a7d78134fcc01336 completed June 25, 2026, 11:56 a.m.
NEDg Description generation batch_6a3d181234f48190bff484199a3adc82 completed June 25, 2026, 11:59 a.m.
NED2 Entity disambiguation (via description) batch_6a3d6383a6848190beddf32135d0b6e5 completed June 25, 2026, 5:21 p.m.
Created at: April 18, 2026, 1:48 a.m.