Triple
T2299407
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eddie Murray |
E51693
|
entity |
| Predicate | careerWalks |
P38561
|
FINISHED |
| Object | 1333 |
—
|
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: 1333 | Statement: [Eddie Murray, careerWalks, 1333]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerWalks Context triple: [Eddie Murray, careerWalks, 1333]
-
A.
careerAssists
Indicates the total number of assists a player has recorded over the entire span of their professional or competitive career.
-
B.
businessCareer
Indicates a relationship where an entity’s professional life, roles, or progression is specifically within the field of business or commerce.
-
C.
careerPath
Indicates the progression or sequence of roles, positions, or occupations that an individual follows over time in their professional life.
-
D.
careerStart
Indicates the point in time when an entity begins its professional career or main occupational activity.
-
E.
careerField
Indicates the professional domain or occupational area in which an entity works or specializes.
- 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_69a88b0a9f248190bcff941463d8f65a |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abcbabf01081908db3b42bc7c60444 |
completed | March 7, 2026, 6:54 a.m. |
| PD | Predicate disambiguation | batch_69abc58ad33c8190b8d68af41b6f5e07 |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abcbab15488190bc8d2345f9d9f2bd |
completed | March 7, 2026, 6:54 a.m. |
Created at: March 4, 2026, 7:49 p.m.