Triple
T6011034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | High Point Market |
E133828
|
entity |
| Predicate | relatedCityNickname |
P58505
|
FINISHED |
| Object | High Point as Furniture Capital of the World |
—
|
LITERAL FINISHED |
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: High Point as Furniture Capital of the World | Statement: [High Point Market, relatedCityNickname, High Point as Furniture Capital of the World]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedCityNickname Context triple: [High Point Market, relatedCityNickname, High Point as Furniture Capital of the World]
-
A.
cityNickNameForFanBase
Indicates that a particular nickname is used by fans to refer to a city or its fan base.
-
B.
associatedWithCityNicknameType
Indicates a relationship where a city is linked to a specific type or category of nickname it is known by.
-
C.
metropolitanAreaNickname
Indicates that a metropolitan area is known by a particular informal or colloquial nickname.
-
D.
touristRegionNickname
Indicates that a tourist region is known by a particular informal or colloquial nickname.
-
E.
nicknameOfTown
chosen
Indicates that one entity is a nickname or informal name used to refer to a particular town.
- F. None of above.
Provenance (3 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_69c0087361a48190905c6b55969852b8 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04f4ffa008190a8ef701b82260219 |
completed | March 22, 2026, 8:21 p.m. |
| PD | Predicate disambiguation | batch_69c049e4daf4819099bf870dc700e0a2 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:06 p.m.