Triple

T16782147
Position Surface form Disambiguated ID Type / Status
Subject Matarranya E407881 entity
Predicate containsSettlement P847 FINISHED
Object La Cerollera
La Cerollera is a small rural municipality in the province of Teruel, Aragon, in northeastern Spain.
 E1234180 NE FINISHED

How this triple was built (4 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: La Cerollera | Statement: [Matarranya, containsSettlement, La Cerollera]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: La Cerollera
Context triple: [Matarranya, containsSettlement, La Cerollera]
  • A. La Llagosta
    La Llagosta is a small suburban municipality in Catalonia, Spain, situated within the metropolitan area of Barcelona.
  • B. Lo Valledor
    Lo Valledor is a metro station on Santiago, Chile’s underground network, serving the city’s Line 6 corridor.
  • C. Lo Valledor
    Lo Valledor is a railway station in Santiago, Chile, serving passengers on the Metrotren Nos commuter rail line.
  • D. Royuela
    Royuela is a small rural municipality in the province of Teruel, Aragon, Spain, known for its scenic natural surroundings and traditional village character.
  • E. La Sagrera
    La Sagrera is a major multimodal transport hub in Barcelona that serves as an interchange between several metro lines, commuter trains, and future high-speed rail services.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: La Cerollera
Triple: [Matarranya, containsSettlement, La Cerollera]
Generated description
La Cerollera is a small rural municipality in the province of Teruel, Aragon, in northeastern Spain.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: La Cerollera
Target entity description: La Cerollera is a small rural municipality in the province of Teruel, Aragon, in northeastern Spain.
  • A. La Llagosta
    La Llagosta is a small suburban municipality in Catalonia, Spain, situated within the metropolitan area of Barcelona.
  • B. Lo Valledor
    Lo Valledor is a metro station on Santiago, Chile’s underground network, serving the city’s Line 6 corridor.
  • C. Lo Valledor
    Lo Valledor is a railway station in Santiago, Chile, serving passengers on the Metrotren Nos commuter rail line.
  • D. Royuela
    Royuela is a small rural municipality in the province of Teruel, Aragon, Spain, known for its scenic natural surroundings and traditional village character.
  • E. La Sagrera
    La Sagrera is a major multimodal transport hub in Barcelona that serves as an interchange between several metro lines, commuter trains, and future high-speed rail services.
  • F. None of above. chosen

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_69d8839270588190886720d9519bbf8f completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3b217b2108190bbba262a3b324509 completed April 18, 2026, 4:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00ab0300e48190ad088cd11098ca34 completed May 10, 2026, 3:57 p.m.
NEDg Description generation batch_6a00aba5fb388190ab98a0c8ee50340b completed May 10, 2026, 4 p.m.
NED2 Entity disambiguation (via description) batch_6a00ac54a11c81909afe9244e9fe0656 completed May 10, 2026, 4:03 p.m.
Created at: April 10, 2026, 5:22 a.m.