Triple
T4335120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cordillera Central |
E97443
|
entity |
| Predicate | tourismAttraction |
P530
|
FINISHED |
| Object |
Sagada
Sagada is a scenic mountain town in the Philippines’ Cordillera region, famed for its hanging coffins, limestone caves, and cool highland climate.
|
E432927
|
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: Sagada | Statement: [Cordillera Central, tourismAttraction, Sagada]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sagada Context triple: [Cordillera Central, tourismAttraction, Sagada]
-
A.
Kabankalan
Kabankalan is a major inland city in the province of Negros Occidental in the Philippines, known as a commercial and agricultural hub in the southern part of the island.
-
B.
Guihulngan
Guihulngan is a coastal city and commercial hub in the northern part of Negros Oriental in the Philippines.
-
C.
Naga City
Naga City is a major urban center in the Bicol Region of the Philippines, known as a cultural, religious, and educational hub.
-
D.
Canlaon
Canlaon is a city in the Philippines known for its proximity to Mount Kanlaon, an active volcano and prominent natural landmark on Negros Island.
-
E.
Moalboal
Moalboal is a coastal town in the Philippines renowned for its vibrant coral reefs, sardine runs, and popular diving and snorkeling spots.
- 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: Sagada Triple: [Cordillera Central, tourismAttraction, Sagada]
Generated description
Sagada is a scenic mountain town in the Philippines’ Cordillera region, famed for its hanging coffins, limestone caves, and cool highland climate.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sagada Target entity description: Sagada is a scenic mountain town in the Philippines’ Cordillera region, famed for its hanging coffins, limestone caves, and cool highland climate.
-
A.
Kabankalan
Kabankalan is a major inland city in the province of Negros Occidental in the Philippines, known as a commercial and agricultural hub in the southern part of the island.
-
B.
Guihulngan
Guihulngan is a coastal city and commercial hub in the northern part of Negros Oriental in the Philippines.
-
C.
Naga City
Naga City is a major urban center in the Bicol Region of the Philippines, known as a cultural, religious, and educational hub.
-
D.
Canlaon
Canlaon is a city in the Philippines known for its proximity to Mount Kanlaon, an active volcano and prominent natural landmark on Negros Island.
-
E.
Moalboal
Moalboal is a coastal town in the Philippines renowned for its vibrant coral reefs, sardine runs, and popular diving and snorkeling spots.
- 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_69b3454662a481908fbcd0bbfaa3a0a4 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35152bfc88190ab5d53ca38f98d8a |
completed | March 12, 2026, 11:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5db9c1a90819083d0889a65f04af2 |
completed | March 14, 2026, 10:05 p.m. |
| NEDg | Description generation | batch_69b5dc7763488190b056ba759ac9fa73 |
completed | March 14, 2026, 10:08 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5dd07854c8190ac55586d245028a6 |
completed | March 14, 2026, 10:11 p.m. |
Created at: March 12, 2026, 11:14 p.m.