Triple
T4403346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Holden Torana LJ |
E93666
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
LJ Torana
LJ Torana is a model of the Holden Torana, a compact Australian car produced by Holden in the early 1970s and popular in both everyday use and motorsport.
|
E438228
|
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: LJ Torana | Statement: [Holden Torana LJ, alsoKnownAs, LJ Torana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LJ Torana Context triple: [Holden Torana LJ, alsoKnownAs, LJ Torana]
-
A.
Citura
Citura is the public transport operator responsible for managing Reims’ urban transit network, including its tramway system, in northeastern France.
-
B.
Griante
Griante is a small lakeside village on Lake Como in Lombardy, Italy, known for its scenic views and historic villas.
-
C.
Lansen
Lansen is the NATO reporting name for the Swedish Saab 32, a Cold War-era jet aircraft used primarily for attack and reconnaissance roles.
-
D.
Biqueli
Biqueli is a small coastal settlement on Atauro Island in East Timor, known for its fishing community and proximity to coral reefs.
-
E.
Rivaz
Rivaz is a picturesque Swiss wine-growing village on the shores of Lake Geneva, renowned for its terraced vineyards within the UNESCO-listed Lavaux region.
- 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: LJ Torana Triple: [Holden Torana LJ, alsoKnownAs, LJ Torana]
Generated description
LJ Torana is a model of the Holden Torana, a compact Australian car produced by Holden in the early 1970s and popular in both everyday use and motorsport.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: LJ Torana Target entity description: LJ Torana is a model of the Holden Torana, a compact Australian car produced by Holden in the early 1970s and popular in both everyday use and motorsport.
-
A.
Citura
Citura is the public transport operator responsible for managing Reims’ urban transit network, including its tramway system, in northeastern France.
-
B.
Griante
Griante is a small lakeside village on Lake Como in Lombardy, Italy, known for its scenic views and historic villas.
-
C.
Lansen
Lansen is the NATO reporting name for the Swedish Saab 32, a Cold War-era jet aircraft used primarily for attack and reconnaissance roles.
-
D.
Biqueli
Biqueli is a small coastal settlement on Atauro Island in East Timor, known for its fishing community and proximity to coral reefs.
-
E.
Rivaz
Rivaz is a picturesque Swiss wine-growing village on the shores of Lake Geneva, renowned for its terraced vineyards within the UNESCO-listed Lavaux region.
- 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_69b345158c748190a2c040fce2da9980 |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b352d1af608190ac06d50433cf24bb |
completed | March 12, 2026, 11:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5f5fc9f508190a31adb0758555dfa |
completed | March 14, 2026, 11:57 p.m. |
| NEDg | Description generation | batch_69b5f9ccfb708190be00532e7f512a0c |
completed | March 15, 2026, 12:14 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5fa9d7fec81908b53c2e45d0c7fe6 |
completed | March 15, 2026, 12:17 a.m. |
Created at: March 12, 2026, 11:28 p.m.