Triple
T3235945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roman eagle |
E67854
|
entity |
| Predicate | lossConsidered |
P1120
|
FINISHED |
| Object | great dishonor |
—
|
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: great dishonor | Statement: [Roman eagle, lossConsidered, great dishonor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lossConsidered Context triple: [Roman eagle, lossConsidered, great dishonor]
-
A.
lossType
Indicates the specific category or nature of a loss associated with an entity or event.
-
B.
losses
Indicates that an entity experiences a decrease in value, quantity, or advantage as a result of some event or comparison.
-
C.
considered
chosen
Indicates that one entity regards, judges, or thinks about another entity in a particular way or context.
-
D.
significantLoss
Indicates that an entity has experienced a major or substantial decrease in value, quantity, or status beyond a normal or minor loss.
-
E.
loserPoints
Indicates the number of points awarded to or accumulated by the losing side in a competitive event or comparison.
- 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_69ad858d27348190abb61c280b4c86a9 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaee05d34819095dbce4db6ac8613 |
completed | March 8, 2026, 5:16 p.m. |
| PD | Predicate disambiguation | batch_69ada4159e0481908cbbdd750f5e08c7 |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:08 p.m.