Triple
T9164002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Muntinlupa |
E219900
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object |
Tunasan
Tunasan is a barangay and district in the southern part of Muntinlupa City in Metro Manila, Philippines.
|
E782894
|
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: Tunasan | Statement: [Muntinlupa, hasDistrict, Tunasan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tunasan Context triple: [Muntinlupa, hasDistrict, Tunasan]
-
A.
Tahkuna
Tahkuna is a coastal settlement in northern Estonia, located on Hiiumaa Island and known for its proximity to the historic Tahkuna Lighthouse.
-
B.
Tafahi
Tafahi is a small, steep volcanic island in the northernmost part of Tonga, known for its conical shape and relative isolation within the Niuas island group.
-
C.
Tulunan
Tulunan is a rural municipality in the province of North Cotabato on the island of Mindanao in the Philippines, known primarily for its agricultural economy.
-
D.
Tzununá
Tzununá is a small, tranquil Mayan village in Guatemala known for its natural beauty, traditional culture, and growing community of eco-lodges and retreat centers.
-
E.
Tamasopo
Tamasopo is a small town in the Huasteca Potosina region of San Luis Potosí, Mexico, known for its lush landscapes and popular nearby waterfalls and natural swimming areas.
- 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: Tunasan Triple: [Muntinlupa, hasDistrict, Tunasan]
Generated description
Tunasan is a barangay and district in the southern part of Muntinlupa City in Metro Manila, Philippines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tunasan Target entity description: Tunasan is a barangay and district in the southern part of Muntinlupa City in Metro Manila, Philippines.
-
A.
Tahkuna
Tahkuna is a coastal settlement in northern Estonia, located on Hiiumaa Island and known for its proximity to the historic Tahkuna Lighthouse.
-
B.
Tafahi
Tafahi is a small, steep volcanic island in the northernmost part of Tonga, known for its conical shape and relative isolation within the Niuas island group.
-
C.
Tulunan
Tulunan is a rural municipality in the province of North Cotabato on the island of Mindanao in the Philippines, known primarily for its agricultural economy.
-
D.
Tzununá
Tzununá is a small, tranquil Mayan village in Guatemala known for its natural beauty, traditional culture, and growing community of eco-lodges and retreat centers.
-
E.
Tamasopo
Tamasopo is a small town in the Huasteca Potosina region of San Luis Potosí, Mexico, known for its lush landscapes and popular nearby waterfalls and natural swimming areas.
- 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_69ca83e3633c81908688a9fa2306ba99 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccaa2d6628819084ac4734650fe912 |
completed | April 1, 2026, 5:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0547df750819095853f21cf740c63 |
completed | April 3, 2026, 11:59 p.m. |
| NEDg | Description generation | batch_69d0554fda40819083ef2d13d6fba905 |
completed | April 4, 2026, 12:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d055ca4fc08190b30e1b31ded51189 |
completed | April 4, 2026, 12:05 a.m. |
Created at: March 30, 2026, 7:21 p.m.