Triple
T22277020
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uptown, Pittsburgh |
E550633
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object |
Soho
Soho is a neighborhood in Pittsburgh, Pennsylvania, historically known as Uptown and situated between Downtown and Oakland.
|
E1528970
|
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: Soho | Statement: [Uptown, Pittsburgh, hasAlternativeName, Soho]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Soho Context triple: [Uptown, Pittsburgh, hasAlternativeName, Soho]
-
A.
Soho
Soho is a vibrant central London district famed for its nightlife, entertainment venues, and diverse cultural scene.
-
B.
Soho
Soho is an inner-city district of Birmingham, England, historically known for its industrial heritage and diverse local community.
-
C.
Soho
Soho is a vibrant dining, nightlife, and entertainment district in Hong Kong known for its steep streets, trendy bars, and international restaurants.
-
D.
Westend
Westend is a residential and commercial locality in Berlin known for its affluent neighborhoods, green spaces, and proximity to the Olympic Stadium.
-
E.
Westend
Westend is a prominent and affluent district in Frankfurt am Main, Germany, known for its elegant residential areas and concentration of banks and corporate offices.
- 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: Soho Triple: [Uptown, Pittsburgh, hasAlternativeName, Soho]
Generated description
Soho is a neighborhood in Pittsburgh, Pennsylvania, historically known as Uptown and situated between Downtown and Oakland.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Soho Target entity description: Soho is a neighborhood in Pittsburgh, Pennsylvania, historically known as Uptown and situated between Downtown and Oakland.
-
A.
Soho
Soho is a vibrant central London district famed for its nightlife, entertainment venues, and diverse cultural scene.
-
B.
Soho
Soho is an inner-city district of Birmingham, England, historically known for its industrial heritage and diverse local community.
-
C.
Soho
Soho is a vibrant dining, nightlife, and entertainment district in Hong Kong known for its steep streets, trendy bars, and international restaurants.
-
D.
Westend
Westend is a residential and commercial locality in Berlin known for its affluent neighborhoods, green spaces, and proximity to the Olympic Stadium.
-
E.
Westend
Westend is a prominent and affluent district in Frankfurt am Main, Germany, known for its elegant residential areas and concentration of banks and corporate offices.
- 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_69e11e44d538819097c6b8f333af3352 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f14ea8a6688190bcd168670420ec59 |
completed | April 29, 2026, 12:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0abca612008190bcb1c38523223ed7 |
completed | May 18, 2026, 7:15 a.m. |
| NEDg | Description generation | batch_6a0ac0d258d88190a73427e02373a4bc |
completed | May 18, 2026, 7:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0ac1e9666c8190b183c069deaf487c |
completed | May 18, 2026, 7:38 a.m. |
Created at: April 16, 2026, 8:40 p.m.