Triple
T5493190
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Detroit Seamount |
E123749
|
entity |
| Predicate | hasBeenDatedBy |
P64436
|
FINISHED |
| Object | radiometric dating |
—
|
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: radiometric dating | Statement: [Detroit Seamount, hasBeenDatedBy, radiometric dating]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBeenDatedBy Context triple: [Detroit Seamount, hasBeenDatedBy, radiometric dating]
-
A.
hasDateWith
Indicates that one entity is scheduled to go on or is engaged in a romantic or social date with another entity.
-
B.
hasHad
Indicates that an entity previously experienced, possessed, or was involved in something at some point in the past.
-
C.
marriedBefore
Indicates that one entity entered into a marriage at an earlier time than the other entity.
-
D.
hasAffairWith
Indicates that one entity is engaged in a secret or illicit romantic or sexual relationship with another entity, typically outside a committed partnership.
-
E.
hasSex
Indicates that one entity engages in sexual activity with another entity.
- 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_69bd464a2d908190869324ce176779c8 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd9281a0148190bb7a8dae9c991b9c |
completed | March 20, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69bd91a8df6481908d1643f7342fe6f0 |
completed | March 20, 2026, 6:27 p.m. |
| PDg | Predicate description generation | batch_69bd925c62a88190ac932444d5170bdd |
completed | March 20, 2026, 6:30 p.m. |
Created at: March 20, 2026, 2:10 p.m.