Triple
T2093189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robert Little |
E32718
|
entity |
| Predicate | serviceNumberOfVictories |
P35904
|
FINISHED |
| Object | over 40 aerial victories |
—
|
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: over 40 aerial victories | Statement: [Robert Little, serviceNumberOfVictories, over 40 aerial victories]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: serviceNumberOfVictories Context triple: [Robert Little, serviceNumberOfVictories, over 40 aerial victories]
-
A.
numberOfWins
Indicates the count of times an entity has achieved victory in a relevant context or competition.
-
B.
gamesWonBy
Indicates the number of games that have been won by a particular entity in a given context.
-
C.
seriesWinningGame
Indicates that a particular game is the decisive or clinching game in which one side wins the overall series.
-
D.
careerWins
Indicates the total number of wins an individual or entity has accumulated over the course of their entire career.
-
E.
seasonRecordWins
Indicates the number of games a team has won during a specific season.
- 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_69a885eba0708190999696a45cbec816 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abba774ca881909f83cf65ffeb24bb |
completed | March 7, 2026, 5:41 a.m. |
| PD | Predicate disambiguation | batch_69abb7b6274081909df36cd7a7c6a675 |
completed | March 7, 2026, 5:29 a.m. |
| PDg | Predicate description generation | batch_69abba2e65b48190bb19cfc4b0f8c462 |
completed | March 7, 2026, 5:39 a.m. |
Created at: March 4, 2026, 7:43 p.m.