Triple
T35882176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States (during early Iran–Iraq War period) |
E1037539
|
entity |
| Predicate | viewedAsThreat |
P108659
|
FINISHED |
| Object | Islamic Republic of Iran |
—
|
NE NERFINISHED |
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: Islamic Republic of Iran | Statement: [United States (during early Iran–Iraq War period), viewedAsThreat, Islamic Republic of Iran]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: viewedAsThreat Context triple: [United States (during early Iran–Iraq War period), viewedAsThreat, Islamic Republic of Iran]
-
A.
recognizesThreat
Indicates that an entity identifies or acknowledges another entity or situation as a potential danger or source of harm.
-
B.
threatTypeEncountered
Indicates that an entity has come across or experienced a specific type of threat.
-
C.
perceivedThreat
chosen
Indicates that one entity regards another entity or situation as potentially harmful, dangerous, or adverse.
-
D.
hasThreats
Indicates that one entity poses or is associated with potential danger, harm, or adverse consequences toward another entity.
-
E.
threatTypeMonitored
Indicates that a particular type of threat is being actively observed, tracked, or watched for by some monitoring process or system.
- 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_69f76e1f4d748190bb55594d8441d70e |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7aa3883d48190b05e3d2da7a017ae |
completed | May 3, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69f7a8d435288190b30b1991fb003121 |
completed | May 3, 2026, 7:58 p.m. |
Created at: May 3, 2026, 4:06 p.m.