Triple
T12522297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wheatbelt region |
E299347
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object |
Trayning
Trayning is a small rural town in Western Australia's Wheatbelt region, known for its grain farming and agricultural services.
|
E986579
|
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: Trayning | Statement: [Wheatbelt region, hasTown, Trayning]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trayning Context triple: [Wheatbelt region, hasTown, Trayning]
-
A.
Training Po
"Training Po" is an instrumental track from the Kung Fu Panda (2008) film score that underscores Po's rigorous martial arts training and character growth.
-
B.
Taller
Taller is a jazz-pop studio album by British singer-songwriter and pianist Jamie Cullum.
-
C.
Trikken
Trikken is the tram system serving Oslo, Norway, forming a key part of the city's public transportation network.
-
D.
TriG
TriG is a serialization format for RDF that extends Turtle to support the representation of named graphs and datasets.
-
E.
Trikka
Trikka (also known as Trikala) is an ancient city in Thessaly, Greece, traditionally regarded as the birthplace and principal cult center of the healing god Asclepius.
- 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: Trayning Triple: [Wheatbelt region, hasTown, Trayning]
Generated description
Trayning is a small rural town in Western Australia's Wheatbelt region, known for its grain farming and agricultural services.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Trayning Target entity description: Trayning is a small rural town in Western Australia's Wheatbelt region, known for its grain farming and agricultural services.
-
A.
Training Po
"Training Po" is an instrumental track from the Kung Fu Panda (2008) film score that underscores Po's rigorous martial arts training and character growth.
-
B.
Taller
Taller is a jazz-pop studio album by British singer-songwriter and pianist Jamie Cullum.
-
C.
Trikken
Trikken is the tram system serving Oslo, Norway, forming a key part of the city's public transportation network.
-
D.
TriG
TriG is a serialization format for RDF that extends Turtle to support the representation of named graphs and datasets.
-
E.
Trikka
Trikka (also known as Trikala) is an ancient city in Thessaly, Greece, traditionally regarded as the birthplace and principal cult center of the healing god Asclepius.
- 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_69d6ada5cdd48190860d9ce30aff69be |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d9545c2aa081908e8a5a94d30e23eb |
completed | April 10, 2026, 7:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64bc159c88190835fea5c0d9ee799 |
completed | May 2, 2026, 7:08 p.m. |
| NEDg | Description generation | batch_69f64def9a6081908c3048f948829051 |
completed | May 2, 2026, 7:18 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f64ea1719c8190b91ffaab60db25ad |
completed | May 2, 2026, 7:21 p.m. |
Created at: April 8, 2026, 9:57 p.m.