Triple
T11049101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Undying Lands |
E261199
|
entity |
| Predicate | traveledToBy |
P97510
|
FINISHED |
| Object | Elves departing Middle-earth |
—
|
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: Elves departing Middle-earth | Statement: [Undying Lands, traveledToBy, Elves departing Middle-earth]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: traveledToBy Context triple: [Undying Lands, traveledToBy, Elves departing Middle-earth]
-
A.
involvedTravelBetween
Indicates a relationship where an entity participates in or is associated with travel occurring between two specified locations.
-
B.
travelsThrough
Indicates that something moves along, passes across, or is routed via a particular path, medium, or location.
-
C.
oftenTravelsTo
Indicates that one entity frequently goes to or visits another location or entity.
-
D.
travelsOn
Indicates that an entity moves or journeys using a particular route, path, or mode of transportation.
-
E.
visitedCountry
Indicates that an entity has traveled to and spent time in a particular country.
- 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79867fa28819094f564273e3ef51d |
completed | April 9, 2026, 12:15 p.m. |
| PD | Predicate disambiguation | batch_69d7440da46c8190a77380d5d747ac9c |
completed | April 9, 2026, 6:15 a.m. |
| PDg | Predicate description generation | batch_69d750c99f9881908ee2b01b6ce4b3a1 |
completed | April 9, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:26 p.m.