Triple
T36547829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | sinking of Beleriand |
E901180
|
entity |
| Predicate | consequenceForMen |
P190528
|
FINISHED |
| Object | Edain settle in Númenor |
—
|
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: Edain settle in Númenor | Statement: [sinking of Beleriand, consequenceForMen, Edain settle in Númenor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: consequenceForMen Context triple: [sinking of Beleriand, consequenceForMen, Edain settle in Númenor]
-
A.
consequenceOfInfluence
Indicates that one event, state, or condition occurs as a result of the influence or impact exerted by another.
-
B.
consequenceInText
Indicates that one event, action, or state is presented in the text as a consequence or result of another.
-
C.
hasConsequence
Indicates that one event, action, or condition leads to or results in another as its outcome or effect.
-
D.
consequenceForRepublic
Indicates that an event, action, or condition results in a specific effect or outcome for a republic.
-
E.
militaryConsequence
Indicates a causal relationship where one event, action, or condition leads to a specific outcome or effect in the military domain (such as changes in capability, strategy, or conflict status).
- 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_69f76e61217081908b79d610fe67b013 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fcc7779d248190afdb348a95375443 |
completed | May 7, 2026, 5:10 p.m. |
| PD | Predicate disambiguation | batch_69fcc58566a0819082d5ea36e03bf0c6 |
completed | May 7, 2026, 5:01 p.m. |
| PDg | Predicate description generation | batch_69fcc73264e08190b0b5917f32226fae |
completed | May 7, 2026, 5:09 p.m. |
Created at: May 3, 2026, 4:11 p.m.