Triple
T3624850
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marla Lerner Tanenbaum |
E76812
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Marla |
E181993
|
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: Marla | Statement: [Marla Lerner Tanenbaum, givenName, Marla]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marla Context triple: [Marla Lerner Tanenbaum, givenName, Marla]
-
A.
Marla
chosen
Marla is a feminine given name most notably borne by American actress and television personality Marla Maples.
-
B.
Marly
Marly is a French locality historically associated with royal architecture and landscape design, notably linked to the works of architect Jules Hardouin-Mansart.
-
C.
Verna
Verna is a feminine given name that gained particular recognition through film editor Verna Fields, known for her work on movies like "Jaws."
-
D.
Loralai
Loralai is a town and district in northern Balochistan, Pakistan, known historically as a regional administrative and trade center.
-
E.
Melissa
Melissa is a small but rapidly growing suburban city in North Texas, located within the Dallas–Fort Worth metropolitan area.
- 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_69ad85dc03948190b35b7189e4175bcc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc2d9845c8190ad65b2471000dfa0 |
completed | March 8, 2026, 6:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4332260cc8190964a15bfee0a3b61 |
completed | March 13, 2026, 3:54 p.m. |
Created at: March 8, 2026, 3:23 p.m.