Triple
T11091290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Davao Oriental |
E262260
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Mati |
E240758
|
NE FINISHED |
How this triple was built (2 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: Mati | Statement: [Davao Oriental, contains, Mati]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mati Context triple: [Davao Oriental, contains, Mati]
-
A.
Mati
chosen
Mati is a coastal city in the Davao Region of the Philippines known for its beaches, surfing spots, and laid-back atmosphere.
-
B.
Arganzuela
Arganzuela is a central district of Madrid, Spain, known for its extensive redevelopment along the Manzanares River and its mix of residential areas, cultural venues, and green spaces.
-
C.
Tinja
Tinja is a small town in northern Tunisia known for its location near Lake Bizerte and its historical and ecological significance.
-
D.
Huerva
The Huerva is a river in northeastern Spain that flows through the province of Zaragoza before joining the Ebro River.
-
E.
Cieneguilla
Cieneguilla is a semi-rural district in the eastern part of Lima, Peru, known for its natural landscapes, country houses, and outdoor recreation areas.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6aa9a40d88190a373e2c7e48285db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d799ebae8c8190987b474adb7ede47 |
completed | April 9, 2026, 12:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e42d66ded88190877a20a10f012d6b |
completed | April 19, 2026, 1:18 a.m. |
Created at: April 8, 2026, 9:27 p.m.