Triple
T8807262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Biscuitmen |
E209562
|
entity |
| Predicate | reflectsLocalIndustry |
P24922
|
FINISHED |
| Object | biscuit-making |
—
|
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: biscuit-making | Statement: [The Biscuitmen, reflectsLocalIndustry, biscuit-making]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reflectsLocalIndustry Context triple: [The Biscuitmen, reflectsLocalIndustry, biscuit-making]
-
A.
localEconomyImpact
Indicates the effect that an action, event, or entity has on the economic conditions, activities, or performance of a specific local area or community.
-
B.
hasNearbyIndustry
Indicates that an entity is located close to one or more industrial facilities or activities.
-
C.
cityIndustryAssociation
Indicates an associative relationship between a city and an industry, such as the industry being present, active, or economically significant in that city.
-
D.
industryContext
chosen
Indicates the industry or sector within which an entity, activity, or relationship is situated or most relevant.
-
E.
hasIndustrialSector
Indicates that an entity is associated with, operates in, or belongs to a particular industrial sector or branch of economic activity.
- 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_69ca836320e48190b5cf585b90a322c4 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5fd2fadc81908b27e9296885af2b |
completed | March 31, 2026, 11:59 p.m. |
| PD | Predicate disambiguation | batch_69cc5c1f28ec8190a34311cb412920c2 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:45 p.m.