Triple
T37142587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nenning |
E920154
|
entity |
| Predicate | nearbyHarbourOrHaven |
P42191
|
FINISHED |
| Object | Havens of the Falas |
—
|
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: Havens of the Falas | Statement: [Nenning, nearbyHarbourOrHaven, Havens of the Falas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyHarbourOrHaven Context triple: [Nenning, nearbyHarbourOrHaven, Havens of the Falas]
-
A.
hasNearbyHarbor
chosen
Indicates that one location has a harbor situated close to it in geographic proximity.
-
B.
nearbyCityOrPort
Indicates that one location is geographically close to a city or port, typically within a short travel distance.
-
C.
associatedHarbour
Indicates a relationship where a place, route, or maritime entity is linked to or served by a particular harbour.
-
D.
harbours
Indicates that one entity provides shelter, refuge, or concealment for another, often by keeping it safe or hidden.
-
E.
harbourName
Indicates the name assigned to a harbour in which an entity is located or associated.
- 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_69f76e9e9d008190a250b0387c992c74 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69ff0491409c8190be40f633a58da0b1 |
completed | May 9, 2026, 9:55 a.m. |
| PD | Predicate disambiguation | batch_69ff040bb5cc81909534c7eee85d5e90 |
completed | May 9, 2026, 9:53 a.m. |
Created at: May 3, 2026, 4:15 p.m.