Triple
T6347242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Port of Fujairah |
E142774
|
entity |
| Predicate | hasAnchorageArea |
P70113
|
FINISHED |
| Object | offshore anchorage for bunkering |
—
|
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: offshore anchorage for bunkering | Statement: [Port of Fujairah, hasAnchorageArea, offshore anchorage for bunkering]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAnchorageArea Context triple: [Port of Fujairah, hasAnchorageArea, offshore anchorage for bunkering]
-
A.
hasLandmarkArea
Indicates that a specified area is designated as the landmark area associated with a particular entity or location.
-
B.
hasAnchorStores
Indicates that a retail property or shopping center includes one or more major anchor stores as primary tenants.
-
C.
hasPier
Indicates that a location or structure possesses or includes a pier as part of its features.
-
D.
hasHarbor
Indicates that a place possesses or contains a harbor for docking or sheltering vessels.
-
E.
hasNearbyHarbor
Indicates that one location has a harbor situated close to it in geographic proximity.
- 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_69c008d5ab108190b346c465696824a9 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067ba2c64819094fa38bb2aeffa6c |
completed | March 22, 2026, 10:05 p.m. |
| PD | Predicate disambiguation | batch_69c060ea1a988190889e47b7e0c819b8 |
completed | March 22, 2026, 9:36 p.m. |
| PDg | Predicate description generation | batch_69c0623bb29081908bfdfb84a07ece90 |
completed | March 22, 2026, 9:42 p.m. |
Created at: March 22, 2026, 4:31 p.m.