Triple
T25011686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Earsdon |
E626004
|
entity |
| Predicate | hasNearbyFormerIndustry |
P124045
|
FINISHED |
| Object | coal mining |
—
|
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: coal mining | Statement: [Earsdon, hasNearbyFormerIndustry, coal mining]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyFormerIndustry Context triple: [Earsdon, hasNearbyFormerIndustry, coal mining]
-
A.
hasNearbyIndustry
Indicates that an entity is located close to one or more industrial facilities or activities.
-
B.
hasNearbyFormerMiningCommunity
Indicates that an entity is located close to a community that previously hosted mining activities but is no longer an active mining site.
-
C.
containsFormerIndustrialAreas
chosen
Indicates that an area includes locations that were previously used for industrial purposes but are no longer active industrial sites.
-
D.
hasHistoricIndustry
Indicates that an entity has been associated with a notable or historically significant industry or industrial activity in the past.
-
E.
locatedInFormerIndustrialArea
Indicates that an entity is situated within an area that was previously used for industrial purposes but is no longer active as such.
- 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_69e2ff27755881908490178e83701160 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f638d11c988190af7fd4572b08e038 |
completed | May 2, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69f63706b6008190993577193c85ff50 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 18, 2026, 6:05 a.m.