Triple
T565909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bandung Conference |
E13552
|
entity |
| Predicate | participantsRegion |
P16438
|
FINISHED |
| Object | Asia |
—
|
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: Asia | Statement: [Bandung Conference, participantsRegion, Asia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: participantsRegion Context triple: [Bandung Conference, participantsRegion, Asia]
-
A.
populationRegion
Indicates that a specified population is located within or associated with a particular geographic region.
-
B.
countryRegion
Indicates that a country is located within, or belongs to, a specific geographic or administrative region.
-
C.
hasRegion
Indicates that an entity includes, contains, or is associated with a specific geographic or administrative region as part of its scope or structure.
-
D.
subregionOf
Indicates that one region is geographically or administratively contained within, and is a part of, another larger region.
-
E.
demographicRegion
Indicates that an entity is associated with, belongs to, or is characterized by a particular geographic or administrative region for demographic purposes.
- 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_69a4933edcf08190b35ecfd6014caee6 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49a74793481908fee3baff0b1d348 |
completed | March 1, 2026, 7:58 p.m. |
| PD | Predicate disambiguation | batch_69a494c183b081909304944aa3d0fe8f |
completed | March 1, 2026, 7:34 p.m. |
| PDg | Predicate description generation | batch_69a4985952a481908b918350ececf484 |
completed | March 1, 2026, 7:49 p.m. |
Created at: March 1, 2026, 7:32 p.m.