Triple
T2375900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cebu |
E46197
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Tabuelan
Tabuelan is a coastal municipality in the province of Cebu in the Philippines, known for its beaches and rural, laid-back atmosphere.
|
E261539
|
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: Tabuelan | Statement: [Cebu, hasPart, Tabuelan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tabuelan Context triple: [Cebu, hasPart, Tabuelan]
-
A.
Tías
Tías is a coastal municipality on the Spanish island of Lanzarote in the Canary Islands, known for the popular tourist resort of Puerto del Carmen.
-
B.
Bobadilla
Bobadilla is a Spanish surname associated with various historical figures, places, and families of Iberian origin.
-
C.
Tababela
Tababela is a rural parish in the Quito Metropolitan District of Ecuador, known for hosting the city’s main air gateway, Mariscal Sucre International Airport.
-
D.
Fabela
Fabela is the maiden surname of Helen Fabela Chávez, a Mexican-American labor leader and wife of civil rights activist César Chávez.
-
E.
Aranzazu
Aranzazu is a small Colombian town located in the mountainous coffee-growing region of the Caldas Department.
- 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: Tabuelan Triple: [Cebu, hasPart, Tabuelan]
Generated description
Tabuelan is a coastal municipality in the province of Cebu in the Philippines, known for its beaches and rural, laid-back atmosphere.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tabuelan Target entity description: Tabuelan is a coastal municipality in the province of Cebu in the Philippines, known for its beaches and rural, laid-back atmosphere.
-
A.
Tías
Tías is a coastal municipality on the Spanish island of Lanzarote in the Canary Islands, known for the popular tourist resort of Puerto del Carmen.
-
B.
Bobadilla
Bobadilla is a Spanish surname associated with various historical figures, places, and families of Iberian origin.
-
C.
Tababela
Tababela is a rural parish in the Quito Metropolitan District of Ecuador, known for hosting the city’s main air gateway, Mariscal Sucre International Airport.
-
D.
Fabela
Fabela is the maiden surname of Helen Fabela Chávez, a Mexican-American labor leader and wife of civil rights activist César Chávez.
-
E.
Aranzazu
Aranzazu is a small Colombian town located in the mountainous coffee-growing region of the Caldas Department.
- 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_69a88a1554a48190a0180682bcf099be |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abc794eee481908163148e1e666d9b |
completed | March 7, 2026, 6:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aea8ac3e80819099065f874f9dc25d |
completed | March 9, 2026, 11:02 a.m. |
| NEDg | Description generation | batch_69aeabd9a5a08190a2c6699576e36c46 |
completed | March 9, 2026, 11:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69aead3299c88190af03577eef126387 |
completed | March 9, 2026, 11:21 a.m. |
Created at: March 4, 2026, 7:57 p.m.