Triple
T23700647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Iceman |
E585584
|
entity |
| Predicate | timeframeOfCrimesDepicted |
P104728
|
FINISHED |
| Object | 1964–1986 |
—
|
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: 1964–1986 | Statement: [The Iceman, timeframeOfCrimesDepicted, 1964–1986]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeframeOfCrimesDepicted Context triple: [The Iceman, timeframeOfCrimesDepicted, 1964–1986]
-
A.
timeframeOfCrimes
chosen
Indicates the period or span of time during which the crimes occurred or were committed.
-
B.
frameForCrime
Indicates causing someone to be falsely perceived or officially treated as responsible for a crime they did not commit.
-
C.
accusationTimeframe
Indicates the time period during which an accusation is made or applies.
-
D.
peakViolencePeriod
Indicates the time period during which violence reaches its highest intensity or frequency within a given context.
-
E.
timePeriodOftenDepicted
Indicates that one entity is a time period that is frequently represented or portrayed in depictions involving the other entity.
- 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_69e24904bd508190abfcb74855de2918 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b682df6881908fe71be9833d3a88 |
completed | April 29, 2026, 7:42 a.m. |
| PD | Predicate disambiguation | batch_69f155d5265881908e43a9696b6a6d0f |
completed | April 29, 2026, 12:50 a.m. |
Created at: April 17, 2026, 6:53 p.m.