Triple

T1577684
Position Surface form Disambiguated ID Type / Status
Subject Podgorica E33689 entity
Predicate formerName P65 FINISHED
Object Titograd
Titograd was the name of Montenegro’s capital city during the socialist Yugoslav era, before it was renamed Podgorica in 1992.
 E205461 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: Titograd | Statement: [Podgorica, formerName, Titograd]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Titograd
Context triple: [Podgorica, formerName, Titograd]
  • A. Niš
    Niš is one of the largest and oldest cities in Serbia, known as a key cultural, economic, and transportation hub in the southern part of the country.
  • B. Banja Luka
    Banja Luka is the second-largest city of Bosnia and Herzegovina and the administrative center of the Republika Srpska entity, known for its riverside setting, Austro-Hungarian architecture, and cultural life.
  • C. Zrenjanin
    Zrenjanin is a city in northern Serbia known as an economic, cultural, and administrative center of the Banat region.
  • D. Zemun
    Zemun is a historic urban municipality of Belgrade, Serbia, known for its preserved old town, Danube riverfront, and distinctive Central European architectural heritage.
  • E. Nikšić
    Nikšić is one of the largest cities in Montenegro, known as an important industrial, cultural, and educational center of the country.
  • 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: Titograd
Triple: [Podgorica, formerName, Titograd]
Generated description
Titograd was the name of Montenegro’s capital city during the socialist Yugoslav era, before it was renamed Podgorica in 1992.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Titograd
Target entity description: Titograd was the name of Montenegro’s capital city during the socialist Yugoslav era, before it was renamed Podgorica in 1992.
  • A. Niš
    Niš is one of the largest and oldest cities in Serbia, known as a key cultural, economic, and transportation hub in the southern part of the country.
  • B. Banja Luka
    Banja Luka is the second-largest city of Bosnia and Herzegovina and the administrative center of the Republika Srpska entity, known for its riverside setting, Austro-Hungarian architecture, and cultural life.
  • C. Zrenjanin
    Zrenjanin is a city in northern Serbia known as an economic, cultural, and administrative center of the Banat region.
  • D. Zemun
    Zemun is a historic urban municipality of Belgrade, Serbia, known for its preserved old town, Danube riverfront, and distinctive Central European architectural heritage.
  • E. Nikšić
    Nikšić is one of the largest cities in Montenegro, known as an important industrial, cultural, and educational center of the country.
  • 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_69a885f27a4c8190a4622252cdf54c00 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a908d571f081908acec43ff2ef112d completed March 5, 2026, 4:38 a.m.
NED1 Entity disambiguation (via context triple) batch_69adc98b2b0081909d10b22d59c5e653 completed March 8, 2026, 7:10 p.m.
NEDg Description generation batch_69adcaed1f788190b14c3e2d2c3036d9 completed March 8, 2026, 7:15 p.m.
NED2 Entity disambiguation (via description) batch_69adcbee97e88190adc1315c0a5013ab completed March 8, 2026, 7:20 p.m.
Created at: March 4, 2026, 7:27 p.m.