Triple
T35130323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philippine–Spanish relations |
E1014417
|
entity |
| Predicate | hasConsulateOfSpainIn |
P206845
|
FINISHED |
| Object | Cebu City |
E46197
|
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: Cebu City | Statement: [Philippine–Spanish relations, hasConsulateOfSpainIn, Cebu City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasConsulateOfSpainIn Context triple: [Philippine–Spanish relations, hasConsulateOfSpainIn, Cebu City]
-
A.
hasConsulateOfPortugalIn
Indicates that a consulate representing Portugal is located within a specified place.
-
B.
hasConsulateOfUnitedStatesIn
Indicates that a consulate of the United States is located in the specified place.
-
C.
haveConsulates
Indicates that one country maintains consular offices or consulates within the territory of another country.
-
D.
haveConsulatesOfAustraliaIn
Indicates that one location hosts consular offices representing Australia within its territory.
-
E.
hasConsulateOfCroatiaIn
Indicates that a location hosts an official consulate representing Croatia.
- 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_69f76dd9c1848190af70d4882a2c1ad7 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3dfab878948190a511d5f0dcdf2432 |
completed | June 26, 2026, 4:06 a.m. |
| PD | Predicate disambiguation | batch_6a037a016960819093ed4990fb4d9d36 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82179081908325a59b8539b3a8 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:02 p.m.