Triple
T8370875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | February Martyrs Stadium |
E197449
|
entity |
| Predicate | hasLocationNear |
P61270
|
FINISHED |
| Object | Mediterranean coast of Libya |
—
|
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: Mediterranean coast of Libya | Statement: [February Martyrs Stadium, hasLocationNear, Mediterranean coast of Libya]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLocationNear Context triple: [February Martyrs Stadium, hasLocationNear, Mediterranean coast of Libya]
-
A.
nearbyLocation
chosen
Indicates that one location is situated close to another location in physical space.
-
B.
locatedNearPass
Indicates that one entity is situated close to a mountain pass or similar passageway.
-
C.
nearbyTo
Indicates that one entity is located close in distance or position to another entity.
-
D.
residesNear
Indicates that one entity lives or is located in close physical proximity to another entity.
-
E.
locationSignedNear
Indicates that an entity’s location is in close physical proximity to another specified place or reference point.
- 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_69ca82f56730819080cec5d991c76f4c |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb80a509dc81909e0ea4c66b21d84f |
completed | March 31, 2026, 8:07 a.m. |
| PD | Predicate disambiguation | batch_69cb70cd04b08190ab5f72afd22a7967 |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:01 p.m.