Triple
T37903884
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spanish Greenland |
E945492
|
entity |
| Predicate | hasImaginedLegalStatus |
P192339
|
FINISHED |
| Object | Spanish overseas province |
—
|
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: Spanish overseas province | Statement: [Spanish Greenland, hasImaginedLegalStatus, Spanish overseas province]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasImaginedLegalStatus Context triple: [Spanish Greenland, hasImaginedLegalStatus, Spanish overseas province]
-
A.
hasImaginedIdentity
Indicates that an entity mentally constructs or adopts a non-actual identity, role, or persona for itself or another entity.
-
B.
hasLegalStatus
Indicates that an entity possesses a particular legal classification, recognition, or standing under law.
-
C.
hasNoLegalStatus
Indicates that the referenced entity lacks any formally recognized legal standing, rights, or status under the applicable legal system.
-
D.
hasLegalStatusOver
Indicates that one entity holds a recognized legal authority, jurisdiction, or standing in relation to another entity.
-
E.
usedLegalStatus
Indicates that one entity applies or relies on the legal status or classification of another entity in a given context.
- 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_69f76ef20bb0819088b5b6ceecb0b8fc |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fd05ba6b2c81909c62b46237d10365 |
completed | May 7, 2026, 9:35 p.m. |
| PD | Predicate disambiguation | batch_69fd03039e48819082b6e12c5453885a |
completed | May 7, 2026, 9:24 p.m. |
| PDg | Predicate description generation | batch_69fd05b965608190a3666410b9f8e125 |
completed | May 7, 2026, 9:35 p.m. |
Created at: May 3, 2026, 4:20 p.m.