Triple
T29095868
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hatter’s region |
E734998
|
entity |
| Predicate | hasNotableIndustryHistorical |
P3008
|
FINISHED |
| Object | hat manufacturing |
—
|
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: hat manufacturing | Statement: [Hatter’s region, hasNotableIndustryHistorical, hat manufacturing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableIndustryHistorical Context triple: [Hatter’s region, hasNotableIndustryHistorical, hat manufacturing]
-
A.
hasNotableHistoricalAssociation
Indicates that there is a significant connection between an entity and an important historical event, figure, period, or development.
-
B.
hasHistoricIndustry
chosen
Indicates that an entity has been associated with a notable or historically significant industry or industrial activity in the past.
-
C.
hasNoMajorIndustry
Indicates that the referenced place or entity does not possess any dominant or significant industrial sector.
-
D.
notableIndustry
Indicates that an entity is significantly recognized or prominent within a specified industry or sector.
-
E.
hasNotableCompany
Indicates that an entity is associated with or linked to a company that is considered notable or significant in some context.
- 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_69f05b0ed66481908f2e864fa550d2f1 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69ff76ac40988190a34d858b5472ee2b |
completed | May 9, 2026, 6:02 p.m. |
| PD | Predicate disambiguation | batch_69ff760a90948190a12fcb80e6e3e14b |
completed | May 9, 2026, 5:59 p.m. |
Created at: April 28, 2026, 11:08 a.m.