Triple
T23743883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abu Kamal |
E586759
|
entity |
| Predicate | oppositeSideCountry |
P86312
|
FINISHED |
| Object | Iraq |
—
|
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: Iraq | Statement: [Abu Kamal, oppositeSideCountry, Iraq]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oppositeSideCountry Context triple: [Abu Kamal, oppositeSideCountry, Iraq]
-
A.
primaryOpposingCountry
Indicates that one country is the main or principal adversary or opponent of another country in a conflict, rivalry, or opposition.
-
B.
oppositeTownCountry
Indicates that two locations are situated in opposing or contrasting town and country settings, such that one is urban while the other is rural.
-
C.
oppositeCityCountry
Indicates that a city and a country are located on opposite sides of the world or in geographically opposing regions relative to each other.
-
D.
oppositeTownAcrossBorder
Indicates that one town is located directly across a border from another town, positioned as its opposite counterpart.
-
E.
countryOnOtherSide
chosen
Indicates that one location is situated across a boundary or dividing feature (such as a border, river, or sea) from another country, on the opposite side.
- 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_69e24908efb08190bf755c3a9b91f222 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1bcbbcf988190b56d74af4b126bd8 |
completed | April 29, 2026, 8:09 a.m. |
| PD | Predicate disambiguation | batch_69f155f012808190a4b1cbc155558ade |
completed | April 29, 2026, 12:50 a.m. |
Created at: April 17, 2026, 7:12 p.m.