Triple
T14465239
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Une Saison en enfer |
E358689
|
entity |
| Predicate | notableSection |
P5600
|
FINISHED |
| Object |
Matin
Matin is a section of Arthur Rimbaud’s poetic work *Une Saison en enfer*, reflecting his intense, visionary style and themes of spiritual crisis.
|
E1101543
|
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: Matin | Statement: [Une Saison en enfer, notableSection, Matin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matin Context triple: [Une Saison en enfer, notableSection, Matin]
-
A.
Mattinata
Mattinata is a coastal town in Italy’s Apulia region, known for its white cliffs, pebble beaches, and scenic setting on the Gargano promontory.
-
B.
Mornant
Mornant is a commune in eastern France located in the Rhône department, known for its rural character and proximity to the Lyon metropolitan area.
-
C.
Morungen
Morungen is the German name for the town now known as Morąg, located in northern Poland.
-
D.
Maiernigg
Maiernigg is a lakeside village on Austria’s Wörthersee, best known as Gustav Mahler’s summer retreat where he composed several major works.
-
E.
Sangin
Sangin is a town in southern Afghanistan that gained notoriety as a major battleground during the Afghan conflict, particularly involving British and U.S. forces.
- 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: Matin Triple: [Une Saison en enfer, notableSection, Matin]
Generated description
Matin is a section of Arthur Rimbaud’s poetic work *Une Saison en enfer*, reflecting his intense, visionary style and themes of spiritual crisis.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Matin Target entity description: Matin is a section of Arthur Rimbaud’s poetic work *Une Saison en enfer*, reflecting his intense, visionary style and themes of spiritual crisis.
-
A.
Mattinata
Mattinata is a coastal town in Italy’s Apulia region, known for its white cliffs, pebble beaches, and scenic setting on the Gargano promontory.
-
B.
Mornant
Mornant is a commune in eastern France located in the Rhône department, known for its rural character and proximity to the Lyon metropolitan area.
-
C.
Morungen
Morungen is the German name for the town now known as Morąg, located in northern Poland.
-
D.
Maiernigg
Maiernigg is a lakeside village on Austria’s Wörthersee, best known as Gustav Mahler’s summer retreat where he composed several major works.
-
E.
Sangin
Sangin is a town in southern Afghanistan that gained notoriety as a major battleground during the Afghan conflict, particularly involving British and U.S. forces.
- 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_69d827966698819082e140837737501d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91f6d4c08190a12e9a5901ee3508 |
completed | April 14, 2026, 7:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd6499c7188190a79411b471cfa7d4 |
completed | May 8, 2026, 4:20 a.m. |
| NEDg | Description generation | batch_69fd68a687dc8190bbb1ea24bfdb674b |
completed | May 8, 2026, 4:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd69982e648190b9af4f2895514c3e |
completed | May 8, 2026, 4:42 a.m. |
Created at: April 10, 2026, 1:19 a.m.