Triple
T5183103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Lee Hancock |
E116965
|
entity |
| Predicate | activeInFieldSince |
P21215
|
FINISHED |
| Object | 1990s |
—
|
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: 1990s | Statement: [John Lee Hancock, activeInFieldSince, 1990s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: activeInFieldSince Context triple: [John Lee Hancock, activeInFieldSince, 1990s]
-
A.
activeInFieldFrom
chosen
Indicates that an entity is professionally or actively engaged in a particular field starting from a specified point in time.
-
B.
activeInYears
Indicates that an entity was active or operational during the specified years or year range.
-
C.
activeFrom
Indicates the starting point in time from which an entity or relationship is considered active or in effect.
-
D.
inUseSince
Indicates that an entity has been actively in use starting from a specified point in time.
-
E.
historicallyActiveIn
Indicates that an entity was active or engaged in significant activities within a particular place or context during a past historical period.
- 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_69bd446140f08190becb93c61158f27f |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd799d50388190bf2b7dfdd90949e9 |
completed | March 20, 2026, 4:45 p.m. |
| PD | Predicate disambiguation | batch_69bd77b7e8b4819092ec3965e11f2dea |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:46 p.m.