Triple
T9419460
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khurais oil field |
E227113
|
entity |
| Predicate | impactOf2019Attacks |
P88758
|
FINISHED |
| Object | temporary disruption of oil production |
—
|
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: temporary disruption of oil production | Statement: [Khurais oil field, impactOf2019Attacks, temporary disruption of oil production]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactOf2019Attacks Context triple: [Khurais oil field, impactOf2019Attacks, temporary disruption of oil production]
-
A.
impactOnIran
Indicates the effect or consequences that an action, event, or policy has on Iran.
-
B.
deadliestTerroristAttackIn
Indicates that a terrorist attack is the most lethal one that has occurred within a specified place or region.
-
C.
locationOfAttacksCondemned
Indicates that a specific location is the site of attacks that have been explicitly denounced or condemned.
-
D.
effectOnPalestinians
Indicates the impact or consequences that an action, event, or condition has on Palestinians.
-
E.
casualtiesImpact
Indicates how the number or severity of casualties affects or influences another factor, situation, or outcome.
- 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_69ca84359e7c819091148ba4b670e436 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd6c23c65081908d1009c26be66533 |
completed | April 1, 2026, 7:04 p.m. |
| PD | Predicate disambiguation | batch_69cca550777c819094e1851a6127cbbc |
completed | April 1, 2026, 4:55 a.m. |
| PDg | Predicate description generation | batch_69cca89b3368819087a3d69270c1f185 |
completed | April 1, 2026, 5:09 a.m. |
Created at: March 30, 2026, 7:48 p.m.