Triple
T35725387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Social Security normal retirement age |
E1032597
|
entity |
| Predicate | valueForBirthYears |
P183864
|
FINISHED |
| Object | 65 years for 1937 and earlier |
—
|
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: 65 years for 1937 and earlier | Statement: [Social Security normal retirement age, valueForBirthYears, 65 years for 1937 and earlier]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: valueForBirthYears Context triple: [Social Security normal retirement age, valueForBirthYears, 65 years for 1937 and earlier]
-
A.
userBirthYear
Indicates the year in which a given user was born.
-
B.
yearOfBirth
Indicates the specific calendar year in which an entity was born.
-
C.
childBirthYear
Indicates the calendar year in which a child of the given parent was born.
-
D.
dateOfBirthPrecision
Indicates the level of exactness (e.g., year, month, day) with which an entity’s date of birth is known or recorded.
-
E.
dateOfBirth
Indicates the specific calendar date on which an individual or entity was born.
- 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_69f76e102b5881909e5d63a30a5cecbe |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a34f8ee08190a040304635539a8f |
completed | May 3, 2026, 7:34 p.m. |
| PD | Predicate disambiguation | batch_69f7a06f125c8190843af194f042a465 |
completed | May 3, 2026, 7:22 p.m. |
| PDg | Predicate description generation | batch_69f7a34e80dc8190980d5b7b0b91341d |
completed | May 3, 2026, 7:34 p.m. |
Created at: May 3, 2026, 4:05 p.m.