Triple
T6659825
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Sanford |
E151445
|
entity |
| Predicate | firstAscentBy |
P1321
|
FINISHED |
| Object | Terris Moore |
E569593
|
NE 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: Terris Moore | Statement: [Mount Sanford, firstAscentBy, Terris Moore]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Terris Moore Context triple: [Mount Sanford, firstAscentBy, Terris Moore]
-
A.
Terris Moore
chosen
Terris Moore was an American mountaineer, explorer, and later university president known for pioneering ascents of major North American peaks.
-
B.
Tim Moore
Tim Moore is a film producer best known for his frequent collaborations with director Clint Eastwood on movies such as "Sully," "American Sniper," and "Gran Torino."
-
C.
Scott Moore
Scott Moore is an American screenwriter best known for co-writing the hit comedy film "The Hangover."
-
D.
Nick Moore
Nick Moore is a British film editor best known for his work on popular films such as "Love Actually."
-
E.
Terry Molloy
Terry Molloy is a British actor best known for playing the villainous Davros in the long-running science fiction series Doctor Who.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c687f5fac48190a09e4838d9c6b45d |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b071cc6c81909d7df1841c645661 |
completed | March 27, 2026, 4:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7007347cc8190a15b4218bb3b7074 |
completed | March 27, 2026, 10:10 p.m. |
Created at: March 27, 2026, 2:02 p.m.