Triple
T28227856
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Puerto Galera |
E711635
|
entity |
| Predicate | distanceFromManilaByFerryAndRoad |
P201129
|
FINISHED |
| Object | approximately 3 to 4 hours |
—
|
LITERAL 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: approximately 3 to 4 hours | Statement: [Puerto Galera, distanceFromManilaByFerryAndRoad, approximately 3 to 4 hours]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromManilaByFerryAndRoad Context triple: [Puerto Galera, distanceFromManilaByFerryAndRoad, approximately 3 to 4 hours]
-
A.
distanceFromManila
Indicates the measured spatial distance between a given entity’s location and the city of Manila.
-
B.
travelTimeFromManilaByCar_hours
Indicates the amount of time, measured in hours, it takes to travel by car from Manila to another location.
-
C.
distanceFromPuertoPrincesaKilometers
Indicates the physical distance, measured in kilometers, between a given location and Puerto Princesa.
-
D.
distanceFromDavaoCity
Indicates the measured spatial distance between a given location and Davao City.
-
E.
travelTimeFromDavaoCity
Indicates the amount of time required to travel from Davao City to another specified location.
- F. None of above. chosen
Provenance (4 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_69efb51dfb048190ada79b745c33b363 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69ffcb5536d88190bfc2e00b854cacfb |
completed | May 10, 2026, 12:03 a.m. |
| PD | Predicate disambiguation | batch_69ffc900c2a081909dea04aa60566923 |
completed | May 9, 2026, 11:53 p.m. |
| PDg | Predicate description generation | batch_69ffcb5428b88190b154776ebcbb81e1 |
completed | May 10, 2026, 12:03 a.m. |
Created at: April 27, 2026, 10:51 p.m.