Triple
T10791314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Eales |
E254583
|
entity |
| Predicate | approxWeightDuringCareer |
P95501
|
FINISHED |
| Object | 110 kg |
—
|
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: 110 kg | Statement: [John Eales, approxWeightDuringCareer, 110 kg]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approxWeightDuringCareer Context triple: [John Eales, approxWeightDuringCareer, 110 kg]
-
A.
hasCareerGamesPlayed
Indicates the total number of games an entity has played over the course of its entire career.
-
B.
hasRankAtPeakOfCareer
Indicates the specific rank or position an entity held at the highest point of its career.
-
C.
playedCareerStartYear
Indicates the calendar year in which an entity’s playing career (such as a professional or competitive role) began.
-
D.
activeYearsInCareer
Indicates the span of time during which an entity was actively engaged in a particular career or professional field.
-
E.
totalCareerPointsNBA
Indicates the total number of points a player has scored over their entire NBA career.
- 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_69d6aa609f008190a294200aefcb7bd5 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d732f6dfcc81909096895c588ef46f |
completed | April 9, 2026, 5:02 a.m. |
| PD | Predicate disambiguation | batch_69d6f316940c819092a96c429629fdef |
completed | April 9, 2026, 12:30 a.m. |
| PDg | Predicate description generation | batch_69d6fa334b8c819082eaf8537084c323 |
completed | April 9, 2026, 1 a.m. |
Created at: April 8, 2026, 9:17 p.m.