Triple
T38456756
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manila–Puerto Princesa |
E912338
|
entity |
| Predicate | touristDestinationAtDestination |
P7335
|
FINISHED |
| Object | Puerto Princesa |
—
|
NE NERFINISHED |
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: Puerto Princesa | Statement: [Manila–Puerto Princesa, touristDestinationAtDestination, Puerto Princesa]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: touristDestinationAtDestination Context triple: [Manila–Puerto Princesa, touristDestinationAtDestination, Puerto Princesa]
-
A.
touristAttractionIn
chosen
Indicates that a place functions as a tourist attraction located within a specified geographic area or entity.
-
B.
alsoAttractsTouristsIn
Indicates that a place, in addition to another, draws or appeals to tourists within a specified location or context.
-
C.
hasTouristAttractionRole
Indicates that an entity serves in the capacity or function of a tourist attraction for another entity (such as a place, organization, or area).
-
D.
nearestTouristDestination
Indicates that one location is the closest tourist destination to another specified location.
-
E.
ridesAttraction
Indicates that an entity participates in experiencing or using an attraction, such as going on a ride at a venue or amusement location.
- F. None of above.
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_69f76e84e2dc81908badf05b3aafa9ea |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fcd313e61c8190b174b331365b803f |
completed | May 7, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69fcd1f6b2e08190bf0300ae7c9ae67a |
completed | May 7, 2026, 5:55 p.m. |
Created at: May 3, 2026, 4:31 p.m.