Triple
T15418734
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Compton, California |
E369316
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
Hub City
Hub City is a nickname for Compton, California, reflecting its central location and role as a major urban and transportation hub in the Los Angeles area.
|
E1156752
|
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: Hub City | Statement: [Compton, California, nickname, Hub City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hub City Context triple: [Compton, California, nickname, Hub City]
-
A.
Hub City
Hub City is the nickname of Crestview, Florida, reflecting its role as a central crossroads and regional center in the Florida Panhandle.
-
B.
Hub City
Hub City is a common nickname for Moncton, a major transportation and commercial center in New Brunswick, Canada.
-
C.
Hub City
Hub City is the nickname for Hagerstown, Maryland, reflecting its historical role as a major regional transportation and commercial center.
-
D.
Hub City
Hub City is the nickname for Lubbock, Texas, a major economic, educational, and healthcare center for the surrounding West Texas region.
-
E.
Hub City
Hub City is a nickname for Colton, California, reflecting its historical role as a major regional transportation and railroad center.
- 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: Hub City Triple: [Compton, California, nickname, Hub City]
Generated description
Hub City is a nickname for Compton, California, reflecting its central location and role as a major urban and transportation hub in the Los Angeles area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hub City Target entity description: Hub City is a nickname for Compton, California, reflecting its central location and role as a major urban and transportation hub in the Los Angeles area.
-
A.
Hub City
Hub City is a nickname for Colton, California, reflecting its historical role as a major regional transportation and railroad center.
-
B.
Hub City
Hub City is the nickname for Hagerstown, Maryland, reflecting its historical role as a major regional transportation and commercial center.
-
C.
Hub City
Hub City is the nickname for Lubbock, Texas, a major economic, educational, and healthcare center for the surrounding West Texas region.
-
D.
Hub City
Hub City is the nickname of Crestview, Florida, reflecting its role as a central crossroads and regional center in the Florida Panhandle.
-
E.
Hub City
Hub City is a popular nickname for Lafayette, Louisiana, reflecting its role as a central economic and cultural hub in the region.
- 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_69d85a1849f48190bf898068b2806fae |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ebce4f48190ba282ecb4fb2f6fa |
completed | April 16, 2026, 1:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff1a78f9cc819097a6ff1e0cbdbcf3 |
completed | May 9, 2026, 11:28 a.m. |
| NEDg | Description generation | batch_69ff1bc3aa2c8190b61d21fa78682ea1 |
completed | May 9, 2026, 11:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff1cbccab48190b9e19b5a09324d8c |
completed | May 9, 2026, 11:38 a.m. |
Created at: April 10, 2026, 3:20 a.m.