Triple
T28291095
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laiya Beach |
E713427
|
entity |
| Predicate | travelTimeFromManilaByCar_hours |
P166871
|
FINISHED |
| Object | approximately 3 to 4 |
—
|
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 | Statement: [Laiya Beach, travelTimeFromManilaByCar_hours, approximately 3 to 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelTimeFromManilaByCar_hours Context triple: [Laiya Beach, travelTimeFromManilaByCar_hours, approximately 3 to 4]
-
A.
approxTravelTimeFromCebuCity
Indicates the estimated amount of time it typically takes to travel from Cebu City to another location.
-
B.
timeFromTagbilaranByCar
Indicates the amount of travel time required to reach a destination from Tagbilaran when traveling by car.
-
C.
distanceFromManila
Indicates the measured spatial distance between a given entity’s location and the city of Manila.
-
D.
approximateTravelTimeFromPalompon
Indicates the estimated duration it takes to travel from Palompon to another specified location.
-
E.
travelTimeFromZamboangaCity
Indicates the duration required to travel from Zamboanga 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_69efb52371d88190a1381c4e58a3b731 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f664aa283c8190a869d0555eff60c6 |
completed | May 2, 2026, 8:55 p.m. |
| PD | Predicate disambiguation | batch_69f663362c008190a22afed262f1e426 |
completed | May 2, 2026, 8:48 p.m. |
| PDg | Predicate description generation | batch_69f6645a615481909b53d94512ecbaf1 |
completed | May 2, 2026, 8:53 p.m. |
Created at: April 27, 2026, 11:29 p.m.