Triple
T25000240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shengli Oilfield |
E625696
|
entity |
| Predicate | hasSupportingCity |
P25162
|
FINISHED |
| Object | Dongying City |
—
|
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: Dongying City | Statement: [Shengli Oilfield, hasSupportingCity, Dongying City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSupportingCity Context triple: [Shengli Oilfield, hasSupportingCity, Dongying City]
-
A.
supportsCity
Indicates that one entity provides assistance, resources, or backing to a city, helping it function, develop, or achieve its goals.
-
B.
hasTargetCity
Indicates that something is directed toward, intended for, or specifically associated with a particular city as its target.
-
C.
supportsCityCluster
Indicates that one entity provides the necessary resources, infrastructure, or services for a specific city cluster to function or develop.
-
D.
supportsTeamInCity
Indicates that one entity provides backing or assistance to a sports team that is based in a particular city.
-
E.
hasAssociatedCity
chosen
Indicates that one entity is linked or related to a specific city, typically as its location, base, or primary area of association.
- 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_69e2ff26c50481908bc82e799c9e6587 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f606c79ad081908369605f72e65ca6 |
completed | May 2, 2026, 2:14 p.m. |
| PD | Predicate disambiguation | batch_69f602ce79ec8190b8336c2b9de18ac7 |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 18, 2026, 6:04 a.m.