Triple
T19990755
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Phantasialand |
E494055
|
entity |
| Predicate | hasAttraction |
P105
|
FINISHED |
| Object |
Talocan
Talocan is an elaborately themed suspended top spin thrill ride at the Phantasialand amusement park in Germany.
|
E1406331
|
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: Talocan | Statement: [Phantasialand, hasAttraction, Talocan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Talocan Context triple: [Phantasialand, hasAttraction, Talocan]
-
A.
Himamaylan
Himamaylan is a coastal component city in the southern part of Negros Occidental in the Philippines, known historically as one of the province’s older settlements.
-
B.
Maguling
Maguling is a barangay (village-level administrative division) of the municipality of Maitum in the province of Sarangani, Philippines.
-
C.
Mayong
Mayong is a village in Assam, India, historically renowned as the "land of black magic" and associated with numerous myths, occult practices, and tantric traditions.
-
D.
Mount Guiting-Guiting
Mount Guiting-Guiting is a rugged, pyramid-shaped mountain in the Philippines renowned among hikers and climbers for its challenging trails and rich biodiversity.
-
E.
Tumauini
Tumauini is a municipality in the Philippine province of Isabela known for its historic brick church and agricultural economy.
- 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: Talocan Triple: [Phantasialand, hasAttraction, Talocan]
Generated description
Talocan is an elaborately themed suspended top spin thrill ride at the Phantasialand amusement park in Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Talocan Target entity description: Talocan is an elaborately themed suspended top spin thrill ride at the Phantasialand amusement park in Germany.
-
A.
Himamaylan
Himamaylan is a coastal component city in the southern part of Negros Occidental in the Philippines, known historically as one of the province’s older settlements.
-
B.
Maguling
Maguling is a barangay (village-level administrative division) of the municipality of Maitum in the province of Sarangani, Philippines.
-
C.
Mayong
Mayong is a village in Assam, India, historically renowned as the "land of black magic" and associated with numerous myths, occult practices, and tantric traditions.
-
D.
Mount Guiting-Guiting
Mount Guiting-Guiting is a rugged, pyramid-shaped mountain in the Philippines renowned among hikers and climbers for its challenging trails and rich biodiversity.
-
E.
Tumauini
Tumauini is a municipality in the Philippine province of Isabela known for its historic brick church and agricultural economy.
- 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65fe00b908190bda6b9a3a3281ec0 |
completed | April 20, 2026, 5:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a080501d1308190917e1842dacd3de9 |
completed | May 16, 2026, 5:47 a.m. |
| NEDg | Description generation | batch_6a0808d323cc819086e6282aeef0f651 |
completed | May 16, 2026, 6:04 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0809652acc819098cc2c4f67b5ab66 |
completed | May 16, 2026, 6:06 a.m. |
Created at: April 11, 2026, 3:31 p.m.