Triple
T14627806
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maxine Tarnow |
E343397
|
entity |
| Predicate | narrativeSettingYearApprox |
P11197
|
FINISHED |
| Object | early 2000s |
—
|
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: early 2000s | Statement: [Maxine Tarnow, narrativeSettingYearApprox, early 2000s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: narrativeSettingYearApprox Context triple: [Maxine Tarnow, narrativeSettingYearApprox, early 2000s]
-
A.
narrativeSettingOfWork
Indicates that a particular place, time, or context serves as the narrative setting in which a work’s story or events occur.
-
B.
timeOfNarrative
chosen
Indicates the specific time or period during which the events of a narrative are set or unfold.
-
C.
storySettingDecade
Indicates the decade in which the events or setting of a story take place.
-
D.
placeOfSetting
Indicates the location or environment where an event, scene, or situation takes place.
-
E.
setInFictionalYear
Indicates that the events or narrative of a work are situated in a specified fictional or non-real calendar year.
- 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_69d822dffc3c8190aa173b90761bffda |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb4a7c8fc81909d10c1f563d7d1e7 |
completed | April 14, 2026, 9:41 p.m. |
| PD | Predicate disambiguation | batch_69de657359c88190b082e3e9f86fc1d7 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:26 a.m.