Triple
T23639074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Elizabeth Land |
E583834
|
entity |
| Predicate | humanPresenceType |
P13931
|
FINISHED |
| Object | temporary research personnel only |
—
|
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 research personnel only | Statement: [Princess Elizabeth Land, humanPresenceType, temporary research personnel only]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: humanPresenceType Context triple: [Princess Elizabeth Land, humanPresenceType, temporary research personnel only]
-
A.
hasHumanPresence
chosen
Indicates that humans are physically present in or occupying a given location, object, or context.
-
B.
airPresence
Indicates that air is present in or around an entity, typically signifying that the entity contains, is surrounded by, or is exposed to air.
-
C.
typeOfPresence
Indicates the manner or mode in which an entity is present or exists in relation to another entity, context, or environment.
-
D.
traditionalPresenceIn
Indicates that an entity has a customary or historically established presence within a particular place, context, or setting.
-
E.
scenePresence
Indicates that an entity is present or appears within a particular scene or context.
- 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_69e248fe1c2c8190ac914d2442ff3d26 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b27fc22c8190abda7398b9fb928c |
completed | April 29, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69f118d7903c8190bb590a71771e93af |
completed | April 28, 2026, 8:30 p.m. |
Created at: April 17, 2026, 6:48 p.m.