Triple
T13234566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yorkshire coalfield |
E315109
|
entity |
| Predicate | labourHistoryRole |
P62110
|
FINISHED |
| Object | focal point of industrial disputes |
—
|
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: focal point of industrial disputes | Statement: [Yorkshire coalfield, labourHistoryRole, focal point of industrial disputes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: labourHistoryRole Context triple: [Yorkshire coalfield, labourHistoryRole, focal point of industrial disputes]
-
A.
labourMovementRole
chosen
Indicates the specific role or function an entity holds within a labour or trade union movement.
-
B.
industrialRevolutionRole
Indicates the role or function an entity played in the context of the Industrial Revolution, such as its contribution, influence, or participation in that historical transformation.
-
C.
roleInCompanyHistory
Indicates that an entity held a specific role or position during a particular period or event in a company's history.
-
D.
roleInBritain
Indicates that an entity holds or has held a specific role, position, or function within the context of Britain.
-
E.
laborMarketRole
Indicates the role, function, or position an entity holds within the labor market, such as its job type, employment status, or economic participation category.
- 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_69d806affc688190a25b6ccc588e9c72 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98d36bdf8819099949b1e0e6902d3 |
completed | April 10, 2026, 11:52 p.m. |
| PD | Predicate disambiguation | batch_69d98bcb21648190aef241de1e7887e2 |
completed | April 10, 2026, 11:46 p.m. |
Created at: April 9, 2026, 9:22 p.m.