Triple
T27256078
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Long Tieng region of Laos |
E687617
|
entity |
| Predicate | populationDuringWar |
P174344
|
FINISHED |
| Object | tens of thousands of inhabitants |
—
|
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: tens of thousands of inhabitants | Statement: [Long Tieng region of Laos, populationDuringWar, tens of thousands of inhabitants]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: populationDuringWar Context triple: [Long Tieng region of Laos, populationDuringWar, tens of thousands of inhabitants]
-
A.
numberOfPrisonersDuringWar
Indicates the count of prisoners held during a specified war or wartime period.
-
B.
preWarPopulation
Indicates the population size of an entity as it existed before a specified war or conflict.
-
C.
stateDuringWar
Indicates that a state or condition exists specifically in the context of, or for the duration of, a war or armed conflict.
-
D.
countryDuringWar
Indicates that a country exists or participates as a relevant actor during a specified war or armed conflict.
-
E.
enemyDuringWar
Indicates that one entity is an enemy of another specifically in the context of a particular war or armed conflict.
- 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_69ef35567e808190a94458cd44ebff0c |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69f6c20f209081909fb9ac8f95069f04 |
completed | May 3, 2026, 3:33 a.m. |
| PD | Predicate disambiguation | batch_69f6bd2415fc81908c23c311aebce66f |
completed | May 3, 2026, 3:12 a.m. |
| PDg | Predicate description generation | batch_69f6c125695c81909704c67bef4ce5b2 |
completed | May 3, 2026, 3:29 a.m. |
Created at: April 27, 2026, 10:49 a.m.