Triple
T4197444
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diane Ladd |
E85986
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Enlightened |
E196817
|
NE 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: Enlightened | Statement: [Diane Ladd, notableWork, Enlightened]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Enlightened Context triple: [Diane Ladd, notableWork, Enlightened]
-
A.
Enlightened
chosen
Enlightened is an American comedy-drama television series that follows a self-destructive executive who undergoes a spiritual awakening and attempts to rebuild her life with newfound idealism.
-
B.
The Enlightened City
The Enlightened City is an honorific title for Medina, reflecting its status as a radiant center of early Islamic faith, scholarship, and the Prophet Muhammad’s community.
-
C.
Sagesse
Sagesse is a collection of deeply spiritual and introspective poems by Paul Verlaine, reflecting his religious crisis and search for inner peace.
-
D.
Wise
Wise is a London-based financial technology company best known for its low-cost international money transfer and multi-currency account services.
-
E.
Wise
Wise is a surname shared by various notable individuals across fields such as entertainment, politics, and academia.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69aed93b89f48190a31f6d57c760e42f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af0360bc8081908ceb2483eef89174 |
completed | March 9, 2026, 5:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b58a0ff2a08190ac7f89d306454ab8 |
completed | March 14, 2026, 4:17 p.m. |
Created at: March 9, 2026, 3:48 p.m.