Triple
T10484567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King City, California |
E247262
|
entity |
| Predicate | originalName |
P65
|
FINISHED |
| Object |
Hog Town
Hog Town was the early name of what is now King City, a small agricultural community in Monterey County, California.
|
E865650
|
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: Hog Town | Statement: [King City, California, originalName, Hog Town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hog Town Context triple: [King City, California, originalName, Hog Town]
-
A.
Hoop City
Hoop City is a nickname for Springfield, Massachusetts, highlighting its rich basketball history and status as the birthplace of the sport.
-
B.
Bricktown
Bricktown is a revitalized former warehouse district in downtown Oklahoma City known for its entertainment venues, restaurants, and canal-side attractions.
-
C.
Bear Town
Bear Town is the traditional nickname of Congleton, a historic market town in Cheshire, England.
-
D.
Kin Town
Kin Town is a coastal municipality in Okinawa, Japan, known for hosting part of the U.S. Marine Corps presence on the island and blending local Okinawan culture with a significant military community.
-
E.
Schmicago
Schmicago is the second season of the musical comedy series "Schmigadoon!", parodying and paying homage to the darker, edgier musicals of the 1960s and 1970s.
- 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: Hog Town Triple: [King City, California, originalName, Hog Town]
Generated description
Hog Town was the early name of what is now King City, a small agricultural community in Monterey County, California.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hog Town Target entity description: Hog Town was the early name of what is now King City, a small agricultural community in Monterey County, California.
-
A.
Hoop City
Hoop City is a nickname for Springfield, Massachusetts, highlighting its rich basketball history and status as the birthplace of the sport.
-
B.
Bricktown
Bricktown is a revitalized former warehouse district in downtown Oklahoma City known for its entertainment venues, restaurants, and canal-side attractions.
-
C.
Bear Town
Bear Town is the traditional nickname of Congleton, a historic market town in Cheshire, England.
-
D.
Kin Town
Kin Town is a coastal municipality in Okinawa, Japan, known for hosting part of the U.S. Marine Corps presence on the island and blending local Okinawan culture with a significant military community.
-
E.
Schmicago
Schmicago is the second season of the musical comedy series "Schmigadoon!", parodying and paying homage to the darker, edgier musicals of the 1960s and 1970s.
- 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_69d381c309b88190af78aa681cf6a4c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d50968a0bc8190a18ba24eb37431d9 |
completed | April 7, 2026, 1:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d8a03c647c81909521fee4a66ec8ac |
completed | April 10, 2026, 7:01 a.m. |
| NEDg | Description generation | batch_69d8a45e5a108190ba8e6ba4af858b19 |
completed | April 10, 2026, 7:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d8a890c6b081908e57cc74f18d788b |
completed | April 10, 2026, 7:36 a.m. |
Created at: April 6, 2026, 12:22 p.m.