Triple
T18537356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steve Murphy |
E452998
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Medellín |
—
|
NE NERFINISHED |
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: Medellín | Statement: [Steve Murphy, workLocation, Medellín]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Medellín Context triple: [Steve Murphy, workLocation, Medellín]
-
A.
Medellín
chosen
Medellín is Colombia’s second-largest city, known for its mountainous setting, innovative urban development, and vibrant cultural life.
-
B.
Medellín
Medellín is a historic town in the Extremadura region of western Spain, known as the birthplace of conquistador Hernán Cortés and for its well-preserved medieval castle overlooking the Guadiana River.
-
C.
Bogotá and Medellín
Bogotá and Medellín are Colombia’s two largest and most important cities, serving as major centers of politics, culture, and commerce in the country.
-
D.
Bucaramanga
Bucaramanga is a major city in northeastern Colombia known for its mountainous setting, pleasant climate, and role as an important commercial and industrial center.
-
E.
Manizales
Manizales is a mountainous Colombian city known for its coffee production, cool climate, and location in the central Andes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d387b5548190aa030dad2cb4947e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e534030bd88190b25b95305a12a0c1 |
completed | April 19, 2026, 7:58 p.m. |
Created at: April 10, 2026, 11:37 a.m.