Triple
T1036644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seamus Heaney |
E22377
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
North
North is a 1975 poetry collection by Nobel laureate Seamus Heaney that explores themes of Irish history, violence, and identity through mythic and archaeological imagery.
|
E122094
|
NE FINISHED |
How this triple was built (4 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: North | Statement: [Seamus Heaney, notableWork, North]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: North Context triple: [Seamus Heaney, notableWork, North]
-
A.
North
North is an English surname historically associated with several notable figures in British politics and society.
-
B.
Northern
Northern is a primary regional train operating company in Northern England, running local and commuter rail services across cities such as Manchester, Leeds, and Newcastle.
-
C.
NW
NW is an abbreviation used as a vehicle registration code associated with the German federal state of North Rhine-Westphalia.
-
D.
North East
North East is a rural town in northeastern Dutchess County, New York, known for its agricultural landscape and the village of Millerton.
-
E.
Nord
Nord is a department in northern France known for its industrial heritage, dense population, and proximity to Belgium.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: North Triple: [Seamus Heaney, notableWork, North]
Generated description
North is a 1975 poetry collection by Nobel laureate Seamus Heaney that explores themes of Irish history, violence, and identity through mythic and archaeological imagery.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: North Target entity description: North is a 1975 poetry collection by Nobel laureate Seamus Heaney that explores themes of Irish history, violence, and identity through mythic and archaeological imagery.
-
A.
North
North is an English surname historically associated with several notable figures in British politics and society.
-
B.
Northern
Northern is a primary regional train operating company in Northern England, running local and commuter rail services across cities such as Manchester, Leeds, and Newcastle.
-
C.
NW
NW is an abbreviation used as a vehicle registration code associated with the German federal state of North Rhine-Westphalia.
-
D.
North East
North East is a rural town in northeastern Dutchess County, New York, known for its agricultural landscape and the village of Millerton.
-
E.
Nord
Nord is a department in northern France known for its industrial heritage, dense population, and proximity to Belgium.
- F. None of above. chosen
Provenance (5 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_69a493d848848190aed4011b34b2e8d3 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b82a1014819085bfc077e24c9742 |
completed | March 1, 2026, 10:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac3bc378fc8190846d5ffce73371dd |
completed | March 7, 2026, 2:52 p.m. |
| NEDg | Description generation | batch_69ac3df28858819091c594a9cb2aab07 |
completed | March 7, 2026, 3:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac3e5b716c8190b95fde14ee6c434a |
completed | March 7, 2026, 3:03 p.m. |
Created at: March 1, 2026, 7:41 p.m.