Triple
T20357961
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baba Amr district |
E496699
|
entity |
| Predicate | damageLevel |
P139151
|
FINISHED |
| Object | severely damaged during bombardment |
—
|
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: severely damaged during bombardment | Statement: [Baba Amr district, damageLevel, severely damaged during bombardment]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: damageLevel Context triple: [Baba Amr district, damageLevel, severely damaged during bombardment]
-
A.
damageLeadsTo
Indicates that one instance of damage causally results in or contributes to another specified outcome or condition.
-
B.
damageEffect
Indicates that one entity causes harm, reduction, or deterioration to another entity or its properties.
-
C.
damageClass
chosen
Indicates the type or category of damage associated with an action, event, or interaction between entities.
-
D.
damageTo
Indicates a relationship where one entity causes harm, loss, or deterioration to another entity.
-
E.
damageAssessedBy
Indicates that the extent or nature of damage to something has been evaluated or determined by a particular agent or authority.
- 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_69e0b4a3f7f48190b37f354574028ca6 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67855c3a88190b88839a47d01184d |
completed | April 20, 2026, 7:02 p.m. |
| PD | Predicate disambiguation | batch_69e57636b4808190bc2855af48a3ccdc |
completed | April 20, 2026, 12:41 a.m. |
Created at: April 16, 2026, 11:25 a.m.