Triple

T27095220
Position Surface form Disambiguated ID Type / Status
Subject Montescudaio E686275 entity
Predicate governingBody P46 FINISHED
Object municipal council of Montescudaio
The municipal council of Montescudaio is the local elected legislative body responsible for making decisions and setting policies for the municipality of Montescudaio in Italy.
E1757478 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: municipal council of Montescudaio | Statement: [Montescudaio, governingBody, municipal council of Montescudaio]
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: municipal council of Montescudaio
Triple: [Montescudaio, governingBody, municipal council of Montescudaio]
Generated description
The municipal council of Montescudaio is the local elected legislative body responsible for making decisions and setting policies for the municipality of Montescudaio in Italy.

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_69ef1489f8b481908e24a1985982bd26 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f623b0875481909576d6809b3a5569 completed May 2, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1248054fd481909b8acca7826f4c2e completed May 24, 2026, 12:36 a.m.
NEDg Description generation batch_6a1248bb58a48190ae84e7b538b10503 completed May 24, 2026, 12:39 a.m.
NED2 Entity disambiguation (via description) batch_6a124973e3c88190898b0cece69419b3 completed May 24, 2026, 12:42 a.m.
Created at: April 27, 2026, 8:43 a.m.