Triple
T7794230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wat Phra Singh |
E180257
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Ho Trai
Ho Trai is the ornate scripture library building within Wat Phra Singh in Chiang Mai, traditionally used to house and protect sacred Buddhist texts.
|
E693979
|
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: Ho Trai | Statement: [Wat Phra Singh, hasPart, Ho Trai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ho Trai Context triple: [Wat Phra Singh, hasPart, Ho Trai]
-
A.
Haad Tien
Haad Tien is a quiet, scenic beach on Thailand’s Ko Pha Ngan island, known for its relaxed atmosphere, clear waters, and natural surroundings.
-
B.
Se Kong
Se Kong is a major river in Southeast Asia that flows through Laos, Vietnam, and Cambodia before joining the Mekong River.
-
C.
Poh Pitu
Poh Pitu was an early capital city of the Medang Kingdom, an ancient Javanese Hindu-Buddhist polity in what is now Indonesia.
-
D.
Laoang
Laoang is a coastal municipality in the province of Northern Samar in the Philippines, known for its island landscapes and fishing communities.
-
E.
Xuan Dieu
Xuan Dieu is a popular street and neighborhood in Hanoi’s Tay Ho District, known for its lakeside views, expatriate community, and vibrant mix of cafes, restaurants, and shops.
- 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: Ho Trai Triple: [Wat Phra Singh, hasPart, Ho Trai]
Generated description
Ho Trai is the ornate scripture library building within Wat Phra Singh in Chiang Mai, traditionally used to house and protect sacred Buddhist texts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ho Trai Target entity description: Ho Trai is the ornate scripture library building within Wat Phra Singh in Chiang Mai, traditionally used to house and protect sacred Buddhist texts.
-
A.
Haad Tien
Haad Tien is a quiet, scenic beach on Thailand’s Ko Pha Ngan island, known for its relaxed atmosphere, clear waters, and natural surroundings.
-
B.
Se Kong
Se Kong is a major river in Southeast Asia that flows through Laos, Vietnam, and Cambodia before joining the Mekong River.
-
C.
Poh Pitu
Poh Pitu was an early capital city of the Medang Kingdom, an ancient Javanese Hindu-Buddhist polity in what is now Indonesia.
-
D.
Laoang
Laoang is a coastal municipality in the province of Northern Samar in the Philippines, known for its island landscapes and fishing communities.
-
E.
Xuan Dieu
Xuan Dieu is a popular street and neighborhood in Hanoi’s Tay Ho District, known for its lakeside views, expatriate community, and vibrant mix of cafes, restaurants, and shops.
- 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_69ca827d22208190b4dc5aa680edcf5d |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cae939c7388190b36d3e746be27a4d |
completed | March 30, 2026, 9:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb13ea96cc819081ac26db3ecf4481 |
completed | March 31, 2026, 12:23 a.m. |
| NEDg | Description generation | batch_69cb1730900c8190bc0322c4b6a3772f |
completed | March 31, 2026, 12:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cb1a3e6da08190bf4f82b59db41333 |
completed | March 31, 2026, 12:50 a.m. |
Created at: March 30, 2026, 4:31 p.m.