Triple
T15571086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norah Lindsay |
E374241
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Norah |
E49090
|
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: Norah | Statement: [Norah Lindsay, givenName, Norah]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Norah Context triple: [Norah Lindsay, givenName, Norah]
-
A.
Norah Larkin
Norah Larkin is the central female protagonist of the 1953 film noir "The Blue Gardenia," whose life unravels after a drunken night leads her to become entangled in a murder investigation.
-
B.
Norah Price
Norah Price is the resilient mechanical engineer and de facto leader portrayed by Kristen Stewart in the 2020 sci‑fi horror film "Underwater," who struggles to survive after a deep-sea drilling station is devastated.
-
C.
NORA
NORA is a U.S. national research framework developed by NIOSH to guide and prioritize occupational safety and health research across key industry sectors.
-
D.
Nora
chosen
Nora is a feminine given name of Latin origin, often used independently or as a diminutive of names like Honora, Eleanor, or Leonora.
-
E.
Maren
Maren is a feminine given name of Scandinavian origin, commonly used in Norway and Denmark.
- 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_69d85ccd575081908909b71a3f3e3a61 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e1de0488190b3639fc25f79d343 |
completed | April 16, 2026, 2:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff4c4440a481909699a7eee25a4b24 |
completed | May 9, 2026, 3:01 p.m. |
Created at: April 10, 2026, 4:10 a.m.