Triple
T3003864
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Longdon |
E81850
|
entity |
| Predicate | approxArgentineKilled |
P38049
|
FINISHED |
| Object | 31 |
—
|
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: 31 | Statement: [Mount Longdon, approxArgentineKilled, 31]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approxArgentineKilled Context triple: [Mount Longdon, approxArgentineKilled, 31]
-
A.
casualtiesArgentineKilled
chosen
Indicates that the relationship specifies the number of Argentine casualties who were killed in a particular event or context.
-
B.
opponentInBattleOfTheRiverPlate
Indicates that two entities were opposing sides facing each other in the Battle of the River Plate.
-
C.
allegedToHaveKilled
Indicates that one entity is claimed or accused, but not proven, to have killed another entity.
-
D.
notableVictims
Indicates that the object is a person or group who is especially well-known or significant as a victim of the subject.
-
E.
casualtiesMexicanKilled
Indicates that the event or action resulted in Mexican individuals being killed as casualties.
- F. None of above.
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_69ad8b1c4de88190a83b7cefaa1f2842 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a149b248190ac4f11afc4871cc1 |
completed | March 8, 2026, 3:47 p.m. |
| PD | Predicate disambiguation | batch_69ad96180eb08190a524c5f458d41382 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:59 p.m.