Triple
T5524162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montbrison |
E144882
|
entity |
| Predicate | historicalRegion |
P915
|
FINISHED |
| Object |
Forez
Forez is a historic region in central France, corresponding largely to the plains and hills around Montbrison in today’s Loire department.
|
E528566
|
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: Forez | Statement: [Montbrison, historicalRegion, Forez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Forez Context triple: [Montbrison, historicalRegion, Forez]
-
A.
de Forest
de Forest is a surname most notably associated with Lee de Forest, an American inventor and early pioneer of radio and electronic communication.
-
B.
Kowen Forest
Kowen Forest is a large pine plantation and recreational forest area east of Canberra in the Australian Capital Territory, popular for activities such as mountain biking, trail running, and orienteering.
-
C.
Froyle
Froyle is a small rural village in Hampshire, England, known for its historic church and traditional English countryside setting.
-
D.
Farlee
Farlee is a given name and surname that serves as an alternative spelling of Farley.
-
E.
Kultida Woods
Kultida Woods is the Thai-born mother of golf legend Tiger Woods, known for her strong influence on his upbringing and cultural heritage.
- 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: Forez Triple: [Montbrison, historicalRegion, Forez]
Generated description
Forez is a historic region in central France, corresponding largely to the plains and hills around Montbrison in today’s Loire department.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Forez Target entity description: Forez is a historic region in central France, corresponding largely to the plains and hills around Montbrison in today’s Loire department.
-
A.
de Forest
de Forest is a surname most notably associated with Lee de Forest, an American inventor and early pioneer of radio and electronic communication.
-
B.
Kowen Forest
Kowen Forest is a large pine plantation and recreational forest area east of Canberra in the Australian Capital Territory, popular for activities such as mountain biking, trail running, and orienteering.
-
C.
Froyle
Froyle is a small rural village in Hampshire, England, known for its historic church and traditional English countryside setting.
-
D.
Farlee
Farlee is a given name and surname that serves as an alternative spelling of Farley.
-
E.
Kultida Woods
Kultida Woods is the Thai-born mother of golf legend Tiger Woods, known for her strong influence on his upbringing and cultural heritage.
- 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_69c008f873a481909b4d9f7e2db3c37d |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f85c8508190a0a089402b49a04f |
completed | March 22, 2026, 4:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c027f6aa1c8190b639c317c7d60f64 |
completed | March 22, 2026, 5:33 p.m. |
| NEDg | Description generation | batch_69c033dc91e08190888fb6e94027fbdb |
completed | March 22, 2026, 6:24 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c03460b21481908b78aa4bdc989d2c |
completed | March 22, 2026, 6:26 p.m. |
Created at: March 22, 2026, 3:34 p.m.