Triple
T38324237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tartar region |
E1036736
|
entity |
| Predicate | hasInfrastructureDamageFrom |
P54661
|
FINISHED |
| Object | armed conflict |
—
|
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: armed conflict | Statement: [Tartar region, hasInfrastructureDamageFrom, armed conflict]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInfrastructureDamageFrom Context triple: [Tartar region, hasInfrastructureDamageFrom, armed conflict]
-
A.
infrastructureDamage
chosen
Indicates damage or destruction affecting physical infrastructure such as buildings, roads, utilities, or other constructed facilities.
-
B.
hasDisaster
Indicates that an entity experiences, is affected by, or is associated with a disaster event.
-
C.
isMajorDamOf
Indicates that one dam is the primary or most significant dam associated with a particular river, reservoir, or water system.
-
D.
numberOfDistrictsHeavilyDamaged
Indicates the count of districts that have sustained severe or heavy damage in a given context or event.
-
E.
areaDestroyed
Indicates that a specified portion or region has been damaged or ruined to the point of destruction.
- 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_69f76e1c16fc8190bde982289dd5106b |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a01531add1c8190b51add7bb046e2cb |
completed | May 11, 2026, 3:55 a.m. |
| PD | Predicate disambiguation | batch_6a014fef3d8c81909b509d51d0c4cdc7 |
completed | May 11, 2026, 3:41 a.m. |
Created at: May 3, 2026, 4:30 p.m.