Triple
T30476529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mộc Bài International Border Gate |
E775457
|
entity |
| Predicate | oppositeCountryFacility |
P169554
|
FINISHED |
| Object | Bavet, Svay Rieng Province, Cambodia |
—
|
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: Bavet, Svay Rieng Province, Cambodia | Statement: [Mộc Bài International Border Gate, oppositeCountryFacility, Bavet, Svay Rieng Province, Cambodia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oppositeCountryFacility Context triple: [Mộc Bài International Border Gate, oppositeCountryFacility, Bavet, Svay Rieng Province, Cambodia]
-
A.
oppositeFacility
chosen
Indicates that one facility is located directly across from or facing another facility.
-
B.
oppositeTownCountry
Indicates that two locations are situated in opposing or contrasting town and country settings, such that one is urban while the other is rural.
-
C.
oppositeCityCountry
Indicates that a city and a country are located on opposite sides of the world or in geographically opposing regions relative to each other.
-
D.
nearbyFacilityCountry
Indicates that a facility is located in or near the specified country.
-
E.
countryOfFacility
Indicates that a facility is located within or belongs to a specific country.
- 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_69f22497341481909c21ba329fadaa6b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f78c61ed4c8190ad84c918fa9af55a |
completed | May 3, 2026, 5:56 p.m. |
| PD | Predicate disambiguation | batch_69f78b8cb3a881909ebaac1b503988c2 |
completed | May 3, 2026, 5:53 p.m. |
Created at: April 29, 2026, 8:12 p.m.