Triple
T18456425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cessnock City |
E450912
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Laguna
Laguna is a small rural locality within the Cessnock local government area in the Hunter Region of New South Wales, Australia.
|
E1325023
|
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: Laguna | Statement: [Cessnock City, contains, Laguna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laguna Context triple: [Cessnock City, contains, Laguna]
-
A.
Laguna
Laguna is a province in the Philippines known for its hot springs, lakeside towns around Laguna de Bay, and as the birthplace of national hero José Rizal.
-
B.
Laguna
Laguna is the internal codename Apple used for its early Macintosh Portable computer model.
-
C.
Lagunas
Lagunas is a municipality and town in the state of Jalisco, Mexico, known for its rural character and proximity to the Sierra de Amula region.
-
D.
Laguna San Rafael
Laguna San Rafael is a glacial lagoon in southern Chile famed for its dramatic icebergs and proximity to the San Rafael Glacier within Laguna San Rafael National Park.
-
E.
Laguna Miscanti
Laguna Miscanti is a high-altitude Andean lake in northern Chile famed for its deep blue waters, surrounding volcanoes, and striking desert landscape.
- 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: Laguna Triple: [Cessnock City, contains, Laguna]
Generated description
Laguna is a small rural locality within the Cessnock local government area in the Hunter Region of New South Wales, Australia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Laguna Target entity description: Laguna is a small rural locality within the Cessnock local government area in the Hunter Region of New South Wales, Australia.
-
A.
Laguna
Laguna is a province in the Philippines known for its hot springs, lakeside towns around Laguna de Bay, and as the birthplace of national hero José Rizal.
-
B.
Laguna
Laguna is the internal codename Apple used for its early Macintosh Portable computer model.
-
C.
Lagunas
Lagunas is a municipality and town in the state of Jalisco, Mexico, known for its rural character and proximity to the Sierra de Amula region.
-
D.
Laguna San Rafael
Laguna San Rafael is a glacial lagoon in southern Chile famed for its dramatic icebergs and proximity to the San Rafael Glacier within Laguna San Rafael National Park.
-
E.
Laguna Miscanti
Laguna Miscanti is a high-altitude Andean lake in northern Chile famed for its deep blue waters, surrounding volcanoes, and striking desert landscape.
- 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_69d8d38345688190b565eac2e4cd7935 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5264ce5948190b57baa2ea71297a9 |
completed | April 19, 2026, 7 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a040fe7cb0481908266ee813d99cf87 |
completed | May 13, 2026, 5:45 a.m. |
| NEDg | Description generation | batch_6a04118472a08190b450dfe756475687 |
completed | May 13, 2026, 5:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0412b3f2288190a4090ae5363f7d9f |
completed | May 13, 2026, 5:57 a.m. |
Created at: April 10, 2026, 11:31 a.m.