Triple
T36586028
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lambug |
E902520
|
entity |
| Predicate | hasNearbyTownCenter |
P36605
|
FINISHED |
| Object | Badian town proper |
—
|
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: Badian town proper | Statement: [Lambug, hasNearbyTownCenter, Badian town proper]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyTownCenter Context triple: [Lambug, hasNearbyTownCenter, Badian town proper]
-
A.
hasNearbyCommercialFacilities
Indicates that a place is located close to one or more commercial facilities, such as shops, restaurants, or other businesses.
-
B.
nearbyUrbanCenter
chosen
Indicates that one location is geographically close to an urban center, such as a city or large town.
-
C.
hasNearbyFacility
Indicates that one entity is located close to or in the vicinity of a particular facility.
-
D.
isFartherFromCityCenterThan
Indicates that one location is at a greater distance from the city center than another location.
-
E.
hasRegionalCenterNearby
Indicates that a regional center is located in close proximity to the referenced entity.
- 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_69f76e6592e88190bac4eb00a46e9df9 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a005e8a2f7c819085bfc6f04b866d87 |
completed | May 10, 2026, 10:31 a.m. |
| PD | Predicate disambiguation | batch_6a005de82ef08190a015b385d1d3443c |
completed | May 10, 2026, 10:28 a.m. |
Created at: May 3, 2026, 4:11 p.m.