Triple
T5493169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Detroit Seamount |
E123749
|
entity |
| Predicate | isAncient |
P64431
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Detroit Seamount, isAncient, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isAncient Context triple: [Detroit Seamount, isAncient, true]
-
A.
hasAncientMember
Indicates that at least one member of a group, set, or collection originates from or belongs to an ancient time period.
-
B.
hasAncientCityState
Indicates that one entity possesses, contains, or is associated with an ancient city-state as part of its historical or geographical context.
-
C.
ancientNameOf
Indicates that one entity is the historical or ancient name by which the other entity was formerly known.
-
D.
isYoungerRemnantOf
Indicates that one entity is the surviving, younger portion or successor of another entity that existed earlier and has largely disappeared or transformed.
-
E.
oneOfTheOldestOn
Indicates that one entity is among the earliest or longest-existing examples within the set defined by 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.